https://wiki.responsibledata.io/api.php?action=feedcontributions&user=Tin&feedformat=atomResponsible Data Wiki - User contributions [en]2024-03-29T15:12:01ZUser contributionsMediaWiki 1.23.4https://wiki.responsibledata.io/RDF-IHRFGRDF-IHRFG2016-01-18T19:31:41Z<p>Tin: /* Hashtags and Twitter accounts */</p>
<hr />
<div><br />
[[File:RDF-IHRFG.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on human rights funders. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtag: #responsibledata<br />
<br />
Twitter accounts:<br />
* @engnroom<br />
* @ingleton<br />
<br />
== Working groups ==<br />
<br />
<br />
<br />
[[Category:RDF IHRFG]]</div>Tinhttps://wiki.responsibledata.io/RDF-IHRFGRDF-IHRFG2016-01-18T19:31:14Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDF-IHRFG.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on human rights funders. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtag: #responsibledata<br />
<br />
<br />
<br />
== Working groups ==<br />
<br />
<br />
<br />
[[Category:RDF IHRFG]]</div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2016-01-18T19:30:42Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Resource Sprint<br />
** RDF Nairobi|RDF Consent<br />
** RDF Manila|RDF HR documentation<br />
** RDLab Photo|RDLab Photography<br />
** Rdfviz | RDF Dataviz<br />
** RDF-IHRFG | RDF HR funders<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/RDF-IHRFGRDF-IHRFG2016-01-18T19:28:12Z<p>Tin: Created page with " thumb This wiki was created for the participants of the Responsible Data Forum on human rights funders. We will use this for ongoing creating, sharing..."</p>
<hr />
<div><br />
[[File:RDF-IHRFG.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on human rights funders. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
<br />
<br />
== Working groups ==<br />
<br />
<br />
<br />
[[Category:RDF IHRFG]]</div>Tinhttps://wiki.responsibledata.io/Main_PageMain Page2016-01-18T19:26:37Z<p>Tin: </p>
<hr />
<div>Welcome to the Responsible Data Forum wiki! We will use this space to collaborate on resources that help practitioners implement responsible data practices.<br />
<br />
What's responsible data? The responsible data community has developed a working definition. We have defined it as:<br />
<br />
The duty to ensure people's right to consent, privacy, security, and ownership around information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness. <br />
<br />
= Responsible Data Forums: =<br />
<br />
[[File:RDFvalidation.png|border|x50px]] [[RDF_Budapest | Responsible Data Resource Sprint]] - Budapest, September 30 to October 1, 2014<br />
<br />
[[File:RDFconsent.png|border|x50px]] [[RDF_Nairobi | Responsible Data in Consent and Crowdsourcing ]] - Nairobi, October 28, 2014<br />
<br />
[[File:HRD.png|border|x50px]] [[RDF_Manila | Responsible Data on Human Rights Documentation ]] - Manila, March 21 to 22, 2015<br />
<br />
[[File:RDF-photo.png|border|x50px]] [[RDLab_Photo | Responsible Data Lab on Documentary Photography ]] - New York, March 28, 2015<br />
<br />
[[File:RDFVIZ-icon.png|border|x50px]] [[Rdfviz | Responsible Data Forum on data visualization ]] - New York, January 15, 2016<br />
<br />
[[File:RDF-IHRFG.png|border|x50px]] [[RDF-IHRFG | Responsible Data Forum on human rights funders ]] - San Francisco, January 19, 2016<br />
<br />
= Responsible Data Discussions: =<br />
<br />
[[File:Convo_bubbles.jpeg|x30px]] '''[https://responsibledata.io/discussion-on-the-risks-and-mitigations-of-releasing-data/ Discussion on the risks and mitigations of releasing data]''' <br />
<br />
Join us Wednesday, August 26 at 10am EDT to discuss the risks and mitigations of releasing data. Sara-Jayne Terp, Data Scientist at Thoughtworks, and formerly Director of Data Projects at Ushahidi, will serve as the fire-starter and facilitator for this discussion.<br />
<br />
>> [[Instructions to join the RDF online discussion | Instructions on how to join this event]]. <br />
<br />
Looking for past events? Here is a [[Responsible Data Hangouts | list of our past Responsible Data discussions]].<br />
<br />
= Responsible Data Resource Wiki Pages: =<br />
<br />
[[Responsible data storytelling index of resources | Responsible data storytelling index of resources]]<br />
<br />
<br />
----<br />
<br />
For more information on the Responsible Data Forum, visit our [https://responsibledata.io/ website].</div>Tinhttps://wiki.responsibledata.io/File:RDF-photo.pngFile:RDF-photo.png2016-01-18T19:26:19Z<p>Tin: </p>
<hr />
<div></div>Tinhttps://wiki.responsibledata.io/Main_PageMain Page2016-01-18T19:24:57Z<p>Tin: </p>
<hr />
<div>Welcome to the Responsible Data Forum wiki! We will use this space to collaborate on resources that help practitioners implement responsible data practices.<br />
<br />
What's responsible data? The responsible data community has developed a working definition. We have defined it as:<br />
<br />
The duty to ensure people's right to consent, privacy, security, and ownership around information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness. <br />
<br />
= Responsible Data Forums: =<br />
<br />
[[File:RDFvalidation.png|border|x50px]] [[RDF_Budapest | Responsible Data Resource Sprint]] - Budapest, September 30 to October 1, 2014<br />
<br />
[[File:RDFconsent.png|border|x50px]] [[RDF_Nairobi | Responsible Data in Consent and Crowdsourcing ]] - Nairobi, October 28, 2014<br />
<br />
[[File:HRD.png|border|x50px]] [[RDF_Manila | Responsible Data on Human Rights Documentation ]] - Manila, March 21 to 22, 2015<br />
<br />
[[File:HRD.png|border|x50px]] [[RDLab_Photo | Responsible Data Lab on Documentary Photography ]] - New York, March 28, 2015<br />
<br />
[[File:RDFVIZ-icon.png|border|x50px]] [[Rdfviz | Responsible Data Forum on data visualization ]] - New York, January 15, 2016<br />
<br />
[[File:RDF-IHRFG.png|border|x50px]] [[RDF-IHRFG | Responsible Data Forum on human rights funders ]] - San Francisco, January 19, 2016<br />
<br />
= Responsible Data Discussions: =<br />
<br />
[[File:Convo_bubbles.jpeg|x30px]] '''[https://responsibledata.io/discussion-on-the-risks-and-mitigations-of-releasing-data/ Discussion on the risks and mitigations of releasing data]''' <br />
<br />
Join us Wednesday, August 26 at 10am EDT to discuss the risks and mitigations of releasing data. Sara-Jayne Terp, Data Scientist at Thoughtworks, and formerly Director of Data Projects at Ushahidi, will serve as the fire-starter and facilitator for this discussion.<br />
<br />
>> [[Instructions to join the RDF online discussion | Instructions on how to join this event]]. <br />
<br />
Looking for past events? Here is a [[Responsible Data Hangouts | list of our past Responsible Data discussions]].<br />
<br />
= Responsible Data Resource Wiki Pages: =<br />
<br />
[[Responsible data storytelling index of resources | Responsible data storytelling index of resources]]<br />
<br />
<br />
----<br />
<br />
For more information on the Responsible Data Forum, visit our [https://responsibledata.io/ website].</div>Tinhttps://wiki.responsibledata.io/File:RDF-IHRFG.pngFile:RDF-IHRFG.png2016-01-18T19:24:41Z<p>Tin: </p>
<hr />
<div></div>Tinhttps://wiki.responsibledata.io/Main_PageMain Page2016-01-18T19:23:26Z<p>Tin: </p>
<hr />
<div>Welcome to the Responsible Data Forum wiki! We will use this space to collaborate on resources that help practitioners implement responsible data practices.<br />
<br />
What's responsible data? The responsible data community has developed a working definition. We have defined it as:<br />
<br />
The duty to ensure people's right to consent, privacy, security, and ownership around information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness. <br />
<br />
= Responsible Data Forums: =<br />
<br />
[[File:RDFvalidation.png|border|x50px]] [[RDF_Budapest | Responsible Data Resource Sprint]] - Budapest, September 30 to October 1, 2014<br />
<br />
[[File:RDFconsent.png|border|x50px]] [[RDF_Nairobi | Responsible Data in Consent and Crowdsourcing ]] - Nairobi, October 28, 2014<br />
<br />
[[File:HRD.png|border|x50px]] [[RDF_Manila | Responsible Data on Human Rights Documentation ]] - Manila, March 21 to 22, 2015<br />
<br />
[[File:HRD.png|border|x50px]] [[RDLab_Photo | Responsible Data Lab on Documentary Photography ]] - New York, March 28, 2015<br />
<br />
[[File:RDFVIZ-icon.png|border|x50px]] [[Rdfviz | Responsible Data Forum on data visualization ]] - New York, January 15, 2016<br />
<br />
[[File:RDFVIZ-icon.png|border|x50px]] [[RDF-IHRFG | Responsible Data Forum on human rights funders ]] - San Francisco, January 19, 2016<br />
<br />
= Responsible Data Discussions: =<br />
<br />
[[File:Convo_bubbles.jpeg|x30px]] '''[https://responsibledata.io/discussion-on-the-risks-and-mitigations-of-releasing-data/ Discussion on the risks and mitigations of releasing data]''' <br />
<br />
Join us Wednesday, August 26 at 10am EDT to discuss the risks and mitigations of releasing data. Sara-Jayne Terp, Data Scientist at Thoughtworks, and formerly Director of Data Projects at Ushahidi, will serve as the fire-starter and facilitator for this discussion.<br />
<br />
>> [[Instructions to join the RDF online discussion | Instructions on how to join this event]]. <br />
<br />
Looking for past events? Here is a [[Responsible Data Hangouts | list of our past Responsible Data discussions]].<br />
<br />
= Responsible Data Resource Wiki Pages: =<br />
<br />
[[Responsible data storytelling index of resources | Responsible data storytelling index of resources]]<br />
<br />
<br />
----<br />
<br />
For more information on the Responsible Data Forum, visit our [https://responsibledata.io/ website].</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T21:04:09Z<p>Tin: /* Theme clustering */</p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
== Theme clustering ==<br />
[[File:Mushon-cluster.jpeg|400px]]<br />
<br />
* [https://www.dropbox.com/s/ehlficyjmlv4cdm/15-01-2016%20Clustering.pdf?dl=0 View all post-its online] <br />
* [https://www.dropbox.com/s/5emkt4ss8igwqid/15-01-2016%20Clustering.zip?dl=0 Download all post-its (large download)]<br />
<br />
== Working groups ==<br />
<br />
=== [[ rdviz-culture | Culture ]] ===<br />
<br />
=== [[ rdviz-literacy | Literacy ]] ===<br />
<br />
=== [[ rdviz-risk | Risk ]] ===<br />
<br />
=== [[ rdviz-transparency | Transparency ]] ===<br />
<br />
=== [[ rdviz-uncertainty | Uncertainty ]] ===<br />
<br />
=== [[ rdviz-goals | Goals]] ===<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T20:12:01Z<p>Tin: /* Theme clustering */</p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
== Theme clustering ==<br />
[[File:Mushon-cluster.jpeg|400px]]<br />
<br />
Download a zip file with all the post-its [https://www.dropbox.com/s/5emkt4ss8igwqid/15-01-2016%20Clustering.zip?dl=0 on this link]<br />
<br />
== Working groups ==<br />
<br />
=== [[ rdviz-culture | Culture ]] ===<br />
<br />
=== [[ rdviz-literacy | Literacy ]] ===<br />
<br />
=== [[ rdviz-risk | Risk ]] ===<br />
<br />
=== [[ rdviz-transparency | Transparency ]] ===<br />
<br />
=== [[ rdviz-uncertainty | Uncertainty ]] ===<br />
<br />
=== [[ rdviz-goals | Goals]] ===<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T20:11:24Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
== Theme clustering ==<br />
[[File:Mushon-cluster.jpeg|400px]]<br />
<br />
Download a zip file with all the post-its [[ https://www.dropbox.com/s/5emkt4ss8igwqid/15-01-2016%20Clustering.zip?dl=0 on this link ]]<br />
<br />
<br />
== Working groups ==<br />
<br />
=== [[ rdviz-culture | Culture ]] ===<br />
<br />
=== [[ rdviz-literacy | Literacy ]] ===<br />
<br />
=== [[ rdviz-risk | Risk ]] ===<br />
<br />
=== [[ rdviz-transparency | Transparency ]] ===<br />
<br />
=== [[ rdviz-uncertainty | Uncertainty ]] ===<br />
<br />
=== [[ rdviz-goals | Goals]] ===<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/File:Mushon-cluster.jpegFile:Mushon-cluster.jpeg2016-01-15T20:08:34Z<p>Tin: </p>
<hr />
<div></div>Tinhttps://wiki.responsibledata.io/Rdviz-goalsRdviz-goals2016-01-15T19:52:36Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/Rdviz-uncertaintyRdviz-uncertainty2016-01-15T19:52:32Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/Rdviz-transparencyRdviz-transparency2016-01-15T19:52:27Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/Rdviz-riskRdviz-risk2016-01-15T19:52:23Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/Rdviz-literacyRdviz-literacy2016-01-15T19:52:19Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/Rdviz-cultureRdviz-culture2016-01-15T19:51:42Z<p>Tin: Created page with " == Outputs == ''Description of a minimum viable product, aspirational output, stretch goals, etc.'' == Notes == == Audience == ''Personas, use cases, context'' == Next ste..."</p>
<hr />
<div><br />
== Outputs ==<br />
''Description of a minimum viable product, aspirational output, stretch goals, etc.''<br />
<br />
== Notes ==<br />
<br />
== Audience ==<br />
''Personas, use cases, context''<br />
<br />
== Next steps ==<br />
<br />
== Contributors ==<br />
<br />
== Resources (we <3 links!) ==</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:35:59Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
<br />
== Working groups ==<br />
<br />
=== [[ rdviz-culture | Culture ]] ===<br />
<br />
=== [[ rdviz-literacy | Literacy ]] ===<br />
<br />
=== [[ rdviz-risk | Risk ]] ===<br />
<br />
=== [[ rdviz-transparency | Transparency ]] ===<br />
<br />
=== [[ rdviz-uncertainty | Uncertainty ]] ===<br />
<br />
=== [[ rdviz-goals | Goals]] ===<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:35:43Z<p>Tin: /* Working groups */</p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
<br />
== Working groups ==<br />
<br />
=== [[ rdviz-culture | Culture ]] ===<br />
<br />
=== [[ rdviz-literacy | Literacy ]] ===<br />
<br />
=== [[ rdviz-risk | Risk ]] ===<br />
<br />
=== [[ rdviz-transparency | Transparency ]] ===<br />
<br />
=== [[ rdviz-uncertainty | Uncertainty ]] ===<br />
<br />
=== [[ rdviz-goals | Goals]]<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:30:59Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
<br />
== Working groups ==<br />
<br />
=== [[ Culture ]] ===<br />
<br />
=== [[ Literacy ]] ===<br />
<br />
=== [[ Risk ]] ===<br />
<br />
=== [[ Power ]] ===<br />
<br />
=== [[ Uncertainty ]] ===<br />
<br />
<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:18:44Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* Misleading viz for advocacy can be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* Bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok to simplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
<br />
== Working groups ==<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:17:52Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* misleading viz for advocacycan be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* a bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok tosimplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
* The process of construction of a dataviz is more important than the outcome<br />
* The eudcational value of a viz/report/data is wholly determined by its impact on future events<br />
* Infographics are not responsible data visualization<br />
*Ethical data visualization discussions should only focus on the visual<br />
* There should be no figurative representation in data visualization<br />
* Convenience samples should not be visualized<br />
* Web mercator is great<br />
* All data should be open for viz<br />
* A good dataviz opens its data and code<br />
* All things can be visualized<br />
* Majority of dataviz is ultimately useless, if not harmful<br />
* Visualization always has a point of view<br />
* It is possible to anonymize data and still have it be useful<br />
* Everyone should learn how to visualize data<br />
<br />
<br />
== Working groups ==<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T19:10:04Z<p>Tin: </p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
[[File:Rdfviz-spectrogram.jpg|thumb]]<br />
<br />
=== Statements we used ===<br />
* misleading viz for advocacycan be justified<br />
* Anyone, regardless of background, should be free and empowered to visualize data and share widely<br />
* Data visualization should always be able to be interpreted without accompanying narrative<br />
* Potential impact is more important than marginal risk<br />
* a bad dataviz is better than no dataviz<br />
* Only rigorous statistical inference should be visualized at all<br />
<br />
=== Other statements ===<br />
* Dataviz should provoke empathy/concern<br />
* It's ok tosimplify data in visualization<br />
* Visualization without uncertainty is useless<br />
* The better the visualization, the less it has a point of view<br />
* Pie charts can be useful<br />
* Dataviz should be fact-checked<br />
* Dataviz should have an emotional impact to be meaningful in storytelling<br />
* Your axes should always be labelled<br />
* Dataviz is the best way of making an argument<br />
* The more people visualize data, the better<br />
* People know how to read dataviz<br />
* Aesthetics are critical to good dataviz<br />
* Indigenous voices don't need to be visualized in this project<br />
* Objectivity is the holy grail of dataviz<br />
<br />
<br />
== Working groups ==<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/File:Rdfviz-spectrogram.jpgFile:Rdfviz-spectrogram.jpg2016-01-15T19:02:48Z<p>Tin: </p>
<hr />
<div></div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2016-01-15T19:00:48Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Resource Sprint<br />
** RDF Nairobi|RDF Consent<br />
** RDF Manila|RDF HR documentation<br />
** RDLab Photo|RDLab Photography<br />
** Rdfviz | RDF Dataviz<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T18:52:40Z<p>Tin: /* Information for participants */</p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
[https://twitter.com/engnroom/lists/rdfviz-attendants/members Twitter list of participants]<br />
<br />
== Spectrogram statements ==<br />
<br />
== Working groups ==<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/Main_PageMain Page2016-01-15T16:12:13Z<p>Tin: /* Responsible Data Forums: */</p>
<hr />
<div>Welcome to the Responsible Data Forum wiki! We will use this space to collaborate on resources that help practitioners implement responsible data practices.<br />
<br />
What's responsible data? The responsible data community has developed a working definition. We have defined it as:<br />
<br />
The duty to ensure people's right to consent, privacy, security, and ownership around information processes of collection, analysis, storage, presentation and reuse of data, while respecting the values of transparency and openness. <br />
<br />
= Responsible Data Forums: =<br />
<br />
[[File:RDFvalidation.png|border|x50px]] [[RDF_Budapest | Responsible Data Resource Sprint]] - Budapest, September 30 to October 1, 2014<br />
<br />
[[File:RDFconsent.png|border|x50px]] [[RDF_Nairobi | Responsible Data in Consent and Crowdsourcing ]] - Nairobi, October 28, 2014<br />
<br />
[[File:HRD.png|border|x50px]] [[RDF_Manila | Responsible Data on Human Rights Documentation ]] - Manila, March 21 to 22, 2015<br />
<br />
[[File:HRD.png|border|x50px]] [[RDLab_Photo | Responsible Data Lab on Documentary Photography ]] - New York, March 28, 2015<br />
<br />
[[File:RDFVIZ-icon.png|border|x50px]] [[Rdfviz | Responsible Data Forum on data visualization ]] - New York, January 15, 2016<br />
<br />
= Responsible Data Discussions: =<br />
<br />
[[File:Convo_bubbles.jpeg|x30px]] '''[https://responsibledata.io/discussion-on-the-risks-and-mitigations-of-releasing-data/ Discussion on the risks and mitigations of releasing data]''' <br />
<br />
Join us Wednesday, August 26 at 10am EDT to discuss the risks and mitigations of releasing data. Sara-Jayne Terp, Data Scientist at Thoughtworks, and formerly Director of Data Projects at Ushahidi, will serve as the fire-starter and facilitator for this discussion.<br />
<br />
>> [[Instructions to join the RDF online discussion | Instructions on how to join this event]]. <br />
<br />
Looking for past events? Here is a [[Responsible Data Hangouts | list of our past Responsible Data discussions]].<br />
<br />
= Responsible Data Resource Wiki Pages: =<br />
<br />
[[Responsible data storytelling index of resources | Responsible data storytelling index of resources]]<br />
<br />
<br />
----<br />
<br />
For more information on the Responsible Data Forum, visit our [https://responsibledata.io/ website].</div>Tinhttps://wiki.responsibledata.io/RdfvizRdfviz2016-01-15T16:10:37Z<p>Tin: Created page with " thumb This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing..."</p>
<hr />
<div><br />
[[File:RDFVIZ-icon.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Forum on Data visualization. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
=== Hashtags and Twitter accounts ===<br />
<br />
Hashtags: #RDFviz and #responsibledata<br />
<br />
== Spectrogram statements ==<br />
<br />
== Working groups ==<br />
<br />
<br />
[[Category:RDF dataviz]]</div>Tinhttps://wiki.responsibledata.io/File:RDFVIZ-icon.pngFile:RDFVIZ-icon.png2016-01-15T16:06:53Z<p>Tin: </p>
<hr />
<div></div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2015-03-31T11:04:47Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Budapest<br />
** RDF Nairobi|RDF Nairobi<br />
** RDF Manila|RDF Manila<br />
** RDLab Photo|RDLab Photography New York<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2015-03-31T11:04:19Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Budapest<br />
** RDF Nairobi|RDF Nairobi<br />
** RDF Manila|RDF Manila<br />
** RDLab Photography New York|RDLab Photo<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2015-03-22T09:15:22Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Budapest<br />
** RDF Nairobi|RDF Nairobi<br />
** RDF Manila|RDF Manila<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/RDF_BudapestRDF Budapest2014-10-31T07:05:34Z<p>Tin: </p>
<hr />
<div>[[File:RDFvalidation.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data Resource Sprint in Budapest. Many of these outputs were sparked from previous Responsible Data Forums. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for facilitators ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
== Hashtags and Twitter accounts ==<br />
<br />
Hashtags: RDFbuda and responsibledata<br />
<br />
Twitter links: [https://twitter.com/engnroom @engnroom] and [https://twitter.com/aspirationtech @aspirationtech]<br />
<br />
== Document capturing ==<br />
<br />
[[The agenda wall]] - Lists of questions and topics that came from the event participants<br />
<br />
== Product outputs ==<br />
<br />
[[Data Risk Checker]] - a project design tool for assessing potential harms associated with specific data points <br />
<br />
[[Framework for consent policies]] - When interacting with people, it is important to gain their consent when at all possible. This page gives an overview of a framework for considering what level of consent is needed, what to consider when attempting to gain that consent, what responsibilities accompany consent, and the like. It is to be used for the creation of studies, campaigns, and programs.<br />
<br />
[[Newbie guide to select hosting]] - A guide for small to medium-sized NGOS on choosing a hosting provider for their website <br />
<br />
[[Resource creator manifesto]] - Declaration of values and commitments for resource-creators to hold ourselves and each other accountable to our stakeholders regarding the protection and preservation of resources developed from and for these stakeholders. <br />
<br />
[[Feeding empirical data into policy making]] - List of moments in policy process in which we can engage <br />
<br />
[[Practical de-identification guide]] - The Basic De-identification Solution Matrix is an editable Google Spreadsheet that list common variable types from the fields of health, education, finance, environmental, political, a list of de-identification solutions for these types of data, and some suggestions about what forms of de-identification are most useful for each type of data. <br />
<br />
[[Data in the project lifecycle]] - A lightweight tool for data project implementers to help plan their projects and check whether they are being responsible with their data at every step, both for planned and emerging risks.<br />
<br />
[[Primer on responsible data in development]] - A framework for how to sell Responsible Data inside of your organization(s). <br />
<br />
[[Atomized security plans for organizations]] - ''(Link to outputs under constructions available here. Description still to come.)''<br />
<br />
[[Digital first aid kit]] ''(Images available, description and notes are forthcoming)''<br />
<br />
== Farmer's Market Resources Lists == <br />
<br />
Below are lists of resources collaboratively created by the RDF participants:<br />
<br />
*[[Responsible data checklists: existing and wishing existed]]<br />
<br />
*[[orgs working on responsible data]]<br />
<br />
*Responsible data [[policies]]: existing and wishing existed<br />
<br />
*Responsible data [[communities of practice]]<br />
<br />
*[[Voices already talking about responsible data]]<br />
<br />
*Responsible data [[wishlist]]<br />
<br />
*[[Upcoming events that should include responsible data]]<br />
<br />
== Day 2 morning conversations ==<br />
<br />
=== Round 1 ===<br />
[[Packaging]] the RDF outputs - Ideas for how we can package these RDF outputs for further collaboration and testing. <br />
<br />
[[Talking about harm]] - A visualization of a discussion on how to talk about harm responsibly.<br />
<br />
[[Collaboration software alternatives]] - Google Drive is not the only option!<br />
<br />
[[Responsible and/or open data]] - A visualization of the relationship between responsible and open data.<br />
<br />
[[Messaging Matrix]] - This is a matrix to support strategic thinking around awareness raising and advocacy for responsible data. Specifically, it helps to sort different target audiences, identify incentives and craft appropriate messaging. <br />
<br />
[[Why should my NGO care about responsible data]] - A definition of 'responsible data' and incentives (carrots and sticks) for NGOs to care about it.<br />
<br />
[[Video and photo evidence]] ''(Information currently being collected)''<br />
<br />
=== Round 2 ===<br />
<br />
[[Frameworks for data sharing]] - Mapping out a possible decision tree for quite a specific scenario: where an NGO is approached with a request to use their data (still a work in progress).<br />
<br />
[[Piloting plan]] - Notes from a discussion on how we test the usefulness of the tools being developed in the Responsible Data Forum.<br />
<br />
[[Responsible Data campaign]] - Notes from discussion and link to existing resources<br />
<br />
[[Responsible visualization]] - What does it mean to visualize information responsibly?<br />
<br />
[[Code of ethics in sex exploitation]] - Brief summary of a discussion on how to respond to sex exploitation in solidarity, not paternalism.<br />
<br />
[[White paper on responsilble data standards]] - Are there minumum responsible data standards that we can all use?<br />
<br />
[[Human Rights documentation]] - The engine room and HURIDOCS are working to put together a Responsible Data Forum on human rights documentation. This event will focus on responsible data challenges (the personal, technical, and organizational) specific to the process of collecting and using documentation on human rights violations. In initial discussions, we have surfaced topics that could be appropriate.</div>Tinhttps://wiki.responsibledata.io/Data_Risk_CheckerData Risk Checker2014-10-31T07:04:53Z<p>Tin: Tin moved page Responsible Data Risk Mapping to Data Risk Checker</p>
<hr />
<div>'''''Categorizing harm levels on knowledge assets to inform mitigation and protection'''''<br />
<br />
== Connection to previous RDFs ==<br />
This output builds upon (and diverges from) work done in the RDF on private sector data.<br />
<br />
= The Output =<br />
<br />
== WTF ==<br />
<br />
== Assumptions ==<br />
=== Three-step process. ===<br />
We assume that the risk checking will occur inside of a three-step process:<br />
<br />
# Data (and responsible data) literacy<br />
# '''Risk checking'''<br />
# Mitigation<br />
<br />
==== Data literacy ====<br />
In order to be able to effectively utilise the risk checking tool, it is assumed that the practitioners understand the basic concepts and components of data, such as metadata, collection strategies, formats and storage types (boolean, integer, geographic coordinates, etc), and that they are comfortable working with data wrangling tools such as spreadsheets. <br />
<br />
Practitioners should also understand the core Responsible Data (talk to Niels, and Mary) principles that apply when collecting data that might pose risks to entities providing the data (data owners). <br />
<br />
==== Mitigation ====<br />
<br />
The risk checking tool only assesses the risks; it does not propose or recommend risk mitigation techniques. It is assumed that risk checking will be followed by a concrete risk mitigation phase that will be informed by the results of the risk checking. <br />
<br />
=== Audience ===<br />
The risk checking is always tailored towards the audience. Thus, it assumes that whoever is using it has a deep knowledge of the audience, its needs and risks. As a recommendation, the audience should always be included in the process.<br />
<br />
=== Data is inherently unsafe ===<br />
As indicated by the recent events, the overarching assumption throughout this process is that data is always under the risk of exposure. The risk checking process is not intended to communicate or build awareness on how to secure data. We recommend reading and implementing best practices when it comes to collection, storage and dissemination of data <br />
<br />
=== Types of threats ===<br />
We also assume that the person using the risk checking tool understands the basic concepts of digital and physical threat: understanding categories, the power of information, understands what threat modelling means and what it is for, etc.<br />
<br />
=== Types of harm ===<br />
To make the assessment a non-exhaustive exercise, we have broadly classified the harms:<br />
<br />
# '''Physical Harm:''' Identifies any harm that directly puts the owner of the data as a target and cause physical damage.<br />
# '''Psychosocial/Emotional Harm:''' Identifies any harm that cause emotional or social damage to the owner of the data or their acquaintances. <br />
# '''Economic Harm:''' Identifies any harm that cause damages to personal and financial assets.<br />
<br />
== Process for generating a Responsible Data Risk Map ==<br />
<br />
=== Types of Harm: ===<br />
* Psycho-Social / Emotional<br />
* Physical<br />
* Economic<br />
<br />
=== 1. Identify the Persons at Risk in the event of exposure ===<br />
Definition of Persons at Risk: Any entity at risk of being by the exposure. Therefore, not restricted to the data owner or collector.<br />
<br />
=== 2. Identify Knowledge Assets that can be extracted from the data collected ===<br />
Definition of Knowledge Assets: Discrete data points, information extracted from collections of discrete data points, information extracted from meta analysis of data points, information extracted from the mashup of the collected data and external data sources.<br />
<br />
=== 3. Evaluate the importance of each knowledge asset to the campaign ===<br />
The importance is used in combination with Risk assessment to determine what data to collect.<br />
Importance is rated on this scale:<br />
* Low Importance: knowledge assets that have little or no relevance to the success of the campaign<br />
* High Importance: knowledge assets that have significant relevance to the success of the campaign<br />
* '''Must Have''': knowledge assets that are crucial to the success of the campaign<br />
<br />
=== 4. For each Type of Harm: ===<br />
Evaluate probability and severity of harm for each type of harm for each person at risk by each knowledge asset<br />
<br />
Probability of Harm:<br />
* Low - Assessed as 49% or less probability of harm<br />
* High - Assessed as 50% or more probability of harm<br />
<br />
Severity of Harm<br />
* Low - Assessed as causing little to no harm to the Person at Risk<br />
* High - Assessed as causing moderate to severe harm to the Person at Risk<br />
* '''No Go''' - Assessed as causing catastrophic harm to the Person at Risk<br />
<br />
The output of this process is a high-level score for each Person at Risk, with detailed matrices for each Type of Harm as supporting documentation.<br />
<br />
== RISK PROFILES of data collectors and data owners ==<br />
<br />
# Severe/catastrophic risk: Clear, present, very high probability, direct threat with catastrophic impacts that cannot be mitigated. Severe risks include denial of civic rights, detainment, imprisonment, disabling physical injury, or death.<br />
<br />
# High risk: Clear, present or future, high probability, direct or indirect threat with medium to high impacts. High risks include denigration, exclusion, access to civic rights, psychosocial distress, social stigma, loss of reputation, loss of livelihood, economic deprivation, moderate to severe physical injury with temporary or permanent effects on basic life functions. High risks threats also include organizational infiltration; personal intimidation, persecution, harassment, targeting for rights violations. High risks also include organizational or team breakdown.<br />
<br />
# Low/moderate risk: Clear, present or future, low to medium probability, direct or indirect threat with low to moderate impacts. Risks with low to moderate impacts include verbal aggression, temporary psychosocial distress, temporary economic deprivation, discrediting, or temporary organizational or team breakdown.<br />
<br />
<br />
=== Migitation/Safety planning ===<br />
<br />
Responsible data practices require safety planning. This identifies actions you can take to address threats to data collectors and data owners. Questions that may help formulate your plan include:<br />
<br />
* What risks can be eliminated entirely?<br />
* Which risks can be mitigated?<br />
* Based on their likelihood and significance, which risks should be addressed first?<br />
* How can those risks be mitigated?<br />
<br />
It is assumed that data collectors and data owners will not be able to address all threats at once. They should be prepared to schedule work on risk-of-harm assessment as well as safety planning alongside project design, implementation, monitoring and evaluation activities, and across the data lifecycle. Risk assessment and safety planning should be repeated as changes come about in the project context or population of data collectors or owners. Safety plan implementation should be monitored for needed adjustments to the plan for different profiles of data collectors or data owners.<br />
<br />
=== THINGS TO KEEP IN MIND ===<br />
<br />
Be inclusive in your planning. A practitioner's own or participants' risks may depend on other people's habits. Having confidential discussions about organizational safety policies and practices is important.<br />
<br />
Be judicious with permissions and access to digital data, software or hardware: Does everyone in the office have access to all the data or devices in that office? Should they?<br />
<br />
== Use cases for validation and testing ==<br />
<br />
* '''NAZRA for human rights:''' piloting the tool with their forthcoming data collection process<br />
* '''Zasto Ne:''' testing the tool against election monitoring data<br />
*<br />
<br />
== Next steps ==<br />
* development of a spreadsheet that automatically maps and colours content according to input, and created charts and visualizations of the broad picture to assist with decision making<br />
* <br />
<br />
== Contributors ==<br />
<br />
Darko Brkan, founder, Zasto Ne<br />
<br />
Jennifer Schulte, researcher<br />
<br />
Mahy Hassaan, campaign and ad-hoc coordinator, NAZRA for feminist studies<br />
<br />
Sajjad Anwar, software developer<br />
<br />
Tin Geber, project manager, the engine room<br />
<br />
Zack Halloran, director, Crowdmap<br />
<br />
== Food for thought ==<br />
* concepts, problems<br />
* questions to ask frequently<br />
* preventions: what do you actually do in concrete terms to prevent these things from happening<br />
* reactions: responsible responses for when things go wrong<br />
<br />
== Resources (we <3 links!) ==<br />
Frontline Defenders, [http://www.scribd.com/doc/26756511/Digital-Rights-and-Security-Human-Rights-Defenders Digital Rights and Security for Human Rights Defenders]</div>Tinhttps://wiki.responsibledata.io/Responsible_Data_Risk_MappingResponsible Data Risk Mapping2014-10-31T07:04:53Z<p>Tin: Tin moved page Responsible Data Risk Mapping to Data Risk Checker</p>
<hr />
<div>#REDIRECT [[Data Risk Checker]]</div>Tinhttps://wiki.responsibledata.io/Data_Risk_CheckerData Risk Checker2014-10-31T07:03:50Z<p>Tin: </p>
<hr />
<div>'''''Categorizing harm levels on knowledge assets to inform mitigation and protection'''''<br />
<br />
== Connection to previous RDFs ==<br />
This output builds upon (and diverges from) work done in the RDF on private sector data.<br />
<br />
= The Output =<br />
<br />
== WTF ==<br />
<br />
== Assumptions ==<br />
=== Three-step process. ===<br />
We assume that the risk checking will occur inside of a three-step process:<br />
<br />
# Data (and responsible data) literacy<br />
# '''Risk checking'''<br />
# Mitigation<br />
<br />
==== Data literacy ====<br />
In order to be able to effectively utilise the risk checking tool, it is assumed that the practitioners understand the basic concepts and components of data, such as metadata, collection strategies, formats and storage types (boolean, integer, geographic coordinates, etc), and that they are comfortable working with data wrangling tools such as spreadsheets. <br />
<br />
Practitioners should also understand the core Responsible Data (talk to Niels, and Mary) principles that apply when collecting data that might pose risks to entities providing the data (data owners). <br />
<br />
==== Mitigation ====<br />
<br />
The risk checking tool only assesses the risks; it does not propose or recommend risk mitigation techniques. It is assumed that risk checking will be followed by a concrete risk mitigation phase that will be informed by the results of the risk checking. <br />
<br />
=== Audience ===<br />
The risk checking is always tailored towards the audience. Thus, it assumes that whoever is using it has a deep knowledge of the audience, its needs and risks. As a recommendation, the audience should always be included in the process.<br />
<br />
=== Data is inherently unsafe ===<br />
As indicated by the recent events, the overarching assumption throughout this process is that data is always under the risk of exposure. The risk checking process is not intended to communicate or build awareness on how to secure data. We recommend reading and implementing best practices when it comes to collection, storage and dissemination of data <br />
<br />
=== Types of threats ===<br />
We also assume that the person using the risk checking tool understands the basic concepts of digital and physical threat: understanding categories, the power of information, understands what threat modelling means and what it is for, etc.<br />
<br />
=== Types of harm ===<br />
To make the assessment a non-exhaustive exercise, we have broadly classified the harms:<br />
<br />
# '''Physical Harm:''' Identifies any harm that directly puts the owner of the data as a target and cause physical damage.<br />
# '''Psychosocial/Emotional Harm:''' Identifies any harm that cause emotional or social damage to the owner of the data or their acquaintances. <br />
# '''Economic Harm:''' Identifies any harm that cause damages to personal and financial assets.<br />
<br />
== Process for generating a Responsible Data Risk Map ==<br />
<br />
=== Types of Harm: ===<br />
* Psycho-Social / Emotional<br />
* Physical<br />
* Economic<br />
<br />
=== 1. Identify the Persons at Risk in the event of exposure ===<br />
Definition of Persons at Risk: Any entity at risk of being by the exposure. Therefore, not restricted to the data owner or collector.<br />
<br />
=== 2. Identify Knowledge Assets that can be extracted from the data collected ===<br />
Definition of Knowledge Assets: Discrete data points, information extracted from collections of discrete data points, information extracted from meta analysis of data points, information extracted from the mashup of the collected data and external data sources.<br />
<br />
=== 3. Evaluate the importance of each knowledge asset to the campaign ===<br />
The importance is used in combination with Risk assessment to determine what data to collect.<br />
Importance is rated on this scale:<br />
* Low Importance: knowledge assets that have little or no relevance to the success of the campaign<br />
* High Importance: knowledge assets that have significant relevance to the success of the campaign<br />
* '''Must Have''': knowledge assets that are crucial to the success of the campaign<br />
<br />
=== 4. For each Type of Harm: ===<br />
Evaluate probability and severity of harm for each type of harm for each person at risk by each knowledge asset<br />
<br />
Probability of Harm:<br />
* Low - Assessed as 49% or less probability of harm<br />
* High - Assessed as 50% or more probability of harm<br />
<br />
Severity of Harm<br />
* Low - Assessed as causing little to no harm to the Person at Risk<br />
* High - Assessed as causing moderate to severe harm to the Person at Risk<br />
* '''No Go''' - Assessed as causing catastrophic harm to the Person at Risk<br />
<br />
The output of this process is a high-level score for each Person at Risk, with detailed matrices for each Type of Harm as supporting documentation.<br />
<br />
== RISK PROFILES of data collectors and data owners ==<br />
<br />
# Severe/catastrophic risk: Clear, present, very high probability, direct threat with catastrophic impacts that cannot be mitigated. Severe risks include denial of civic rights, detainment, imprisonment, disabling physical injury, or death.<br />
<br />
# High risk: Clear, present or future, high probability, direct or indirect threat with medium to high impacts. High risks include denigration, exclusion, access to civic rights, psychosocial distress, social stigma, loss of reputation, loss of livelihood, economic deprivation, moderate to severe physical injury with temporary or permanent effects on basic life functions. High risks threats also include organizational infiltration; personal intimidation, persecution, harassment, targeting for rights violations. High risks also include organizational or team breakdown.<br />
<br />
# Low/moderate risk: Clear, present or future, low to medium probability, direct or indirect threat with low to moderate impacts. Risks with low to moderate impacts include verbal aggression, temporary psychosocial distress, temporary economic deprivation, discrediting, or temporary organizational or team breakdown.<br />
<br />
<br />
=== Migitation/Safety planning ===<br />
<br />
Responsible data practices require safety planning. This identifies actions you can take to address threats to data collectors and data owners. Questions that may help formulate your plan include:<br />
<br />
* What risks can be eliminated entirely?<br />
* Which risks can be mitigated?<br />
* Based on their likelihood and significance, which risks should be addressed first?<br />
* How can those risks be mitigated?<br />
<br />
It is assumed that data collectors and data owners will not be able to address all threats at once. They should be prepared to schedule work on risk-of-harm assessment as well as safety planning alongside project design, implementation, monitoring and evaluation activities, and across the data lifecycle. Risk assessment and safety planning should be repeated as changes come about in the project context or population of data collectors or owners. Safety plan implementation should be monitored for needed adjustments to the plan for different profiles of data collectors or data owners.<br />
<br />
=== THINGS TO KEEP IN MIND ===<br />
<br />
Be inclusive in your planning. A practitioner's own or participants' risks may depend on other people's habits. Having confidential discussions about organizational safety policies and practices is important.<br />
<br />
Be judicious with permissions and access to digital data, software or hardware: Does everyone in the office have access to all the data or devices in that office? Should they?<br />
<br />
== Use cases for validation and testing ==<br />
<br />
* '''NAZRA for human rights:''' piloting the tool with their forthcoming data collection process<br />
* '''Zasto Ne:''' testing the tool against election monitoring data<br />
*<br />
<br />
== Next steps ==<br />
* development of a spreadsheet that automatically maps and colours content according to input, and created charts and visualizations of the broad picture to assist with decision making<br />
* <br />
<br />
== Contributors ==<br />
<br />
Darko Brkan, founder, Zasto Ne<br />
<br />
Jennifer Schulte, researcher<br />
<br />
Mahy Hassaan, campaign and ad-hoc coordinator, NAZRA for feminist studies<br />
<br />
Sajjad Anwar, software developer<br />
<br />
Tin Geber, project manager, the engine room<br />
<br />
Zack Halloran, director, Crowdmap<br />
<br />
== Food for thought ==<br />
* concepts, problems<br />
* questions to ask frequently<br />
* preventions: what do you actually do in concrete terms to prevent these things from happening<br />
* reactions: responsible responses for when things go wrong<br />
<br />
== Resources (we <3 links!) ==<br />
Frontline Defenders, [http://www.scribd.com/doc/26756511/Digital-Rights-and-Security-Human-Rights-Defenders Digital Rights and Security for Human Rights Defenders]</div>Tinhttps://wiki.responsibledata.io/RDF_NairobiRDF Nairobi2014-10-30T15:04:06Z<p>Tin: </p>
<hr />
<div>[[File:RDFconsent.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data in consent and crowdsourcing in Nairobi. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
== Hashtags and Twitter accounts ==<br />
<br />
Hashtags: #RDFNairobi and #responsibledata<br />
<br />
== Session Visuals ==<br />
<br />
[[RDF Nairobi agenda wall]] - Lists of questions and topics that came from the event participants<br />
<br />
[[RDF Nairobi informed consent | RDF Nairobi - components of informed consent]]<br />
<br />
[[RDF Nairobi consent-ish |RDF Nairobi "consent-ish" (when informed consent is and isn't appropriate)]]<br />
<br />
== Outputs in progress ==<br />
<br />
* [[tech4consent | Tech 4 Consent]]<br />
* [[dutyofcare | Duty Of Care]]<br />
* [[educatingdatasubjects | Educating Data Subjects]]<br />
* [[identifyingrisk | Identifying Risk]]<br />
* [[Policy and Consent]]<br />
<br />
[[Category:RDF Nairobi]]</div>Tinhttps://wiki.responsibledata.io/RDF_NairobiRDF Nairobi2014-10-30T15:02:27Z<p>Tin: </p>
<hr />
<div>[[File:RDFconsent.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data in consent and crowdsourcing in Nairobi. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
== Hashtags and Twitter accounts ==<br />
<br />
Hashtags: #RDFNairobi and #responsibledata<br />
<br />
== Session Visuals ==<br />
<br />
[[RDF Nairobi agenda wall]] - Lists of questions and topics that came from the event participants<br />
<br />
[[RDF Nairobi - informed consent | RDF Nairobi - components of informed consent]]<br />
<br />
[[RDF Nairobi consent-ish |RDF Nairobi "consent-ish" (when informed consent is and isn't appropriate)]]<br />
<br />
== Outputs in progress ==<br />
<br />
* [[tech4consent | Tech 4 Consent]]<br />
* [[dutyofcare | Duty Of Care]]<br />
* [[educatingdatasubjects | Educating Data Subjects]]<br />
* [[identifyingrisk | Identifying Risk]]<br />
* [[Policy and Consent]]<br />
<br />
[[Category:RDF Nairobi]]</div>Tinhttps://wiki.responsibledata.io/RDF_NairobiRDF Nairobi2014-10-30T14:58:42Z<p>Tin: </p>
<hr />
<div>[[File:RDFconsent.png|thumb]]<br />
This wiki was created for the participants of the Responsible Data in consent and crowdsourcing in Nairobi. We will use this for ongoing creating, sharing and collaborating.<br />
<br />
== Information for participants ==<br />
Please make sure to send all notes and materials that are not already captured in the wiki to [mailto:notes@responsibledata.io notes@responsibledata.io]<br />
<br />
== Hashtags and Twitter accounts ==<br />
<br />
Hashtags: #RDFNairobi and #responsibledata<br />
<br />
== Session Visuals ==<br />
<br />
[[RDF Nairobi agenda wall]] - Lists of questions and topics that came from the event participants<br />
<br />
[[RDF Nairobi- components of informed consent]]<br />
<br />
[[RDF Nairobi "consent-ish" (when informed consent is and isn't appropriate)]]<br />
<br />
== Outputs in progress ==<br />
<br />
* [[tech4consent | Tech 4 Consent]]<br />
* [[dutyofcare | Duty Of Care]]<br />
* [[educatingdatasubjects | Educating Data Subjects]]<br />
* [[identifyingrisk | Identifying Risk]]<br />
* [[Policy and Consent]]<br />
<br />
[[Category:RDF Nairobi]]</div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2014-10-30T14:57:44Z<p>Tin: </p>
<hr />
<div>* Forums<br />
** RDF Budapest|RDF Budapest<br />
** RDF Nairobi|RDF Nairobi<br />
<br />
* navigation<br />
** mainpage|mainpage-description<br />
** recentchanges-url|recentchanges<br />
** randompage-url|randompage<br />
** helppage|help<br />
<br />
* SEARCH<br />
* TOOLBOX<br />
* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/MediaWiki:SidebarMediaWiki:Sidebar2014-10-30T14:56:44Z<p>Tin: </p>
<hr />
<div>* navigation<br />
** mainpage|mainpage-description<br />
<br />
* Forums<br />
** RDF Budapest|RDF Budapest<br />
** RDF Nairobi|RDF Nairobi<br />
<br />
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* LANGUAGES</div>Tinhttps://wiki.responsibledata.io/RDF_Nairobi_agenda_wallRDF Nairobi agenda wall2014-10-30T09:29:03Z<p>Tin: Created page with "{| class="wikitable" ! style="font-weight: bold;" | Technology ! style="font-weight: bold;" | Public and publicly accessible data ! style="font-weight: bold;" | Using data ! s..."</p>
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<div>{| class="wikitable"<br />
! style="font-weight: bold;" | Technology<br />
! style="font-weight: bold;" | Public and publicly accessible data<br />
! style="font-weight: bold;" | Using data<br />
! style="font-weight: bold;" | Data protection<br />
! style="font-weight: bold;" | Legal frameworks<br />
! style="font-weight: bold;" | Risks and danger<br />
! style="font-weight: bold;" | Ethics<br />
! style="font-weight: bold;" | Incentives<br />
! style="font-weight: bold;" | Privacy<br />
! style="font-weight: bold;" | Consent-ish (sorta, kinda)<br />
! style="font-weight: bold;" | Collective/aggregate consent<br />
! style="font-weight: bold;" | "Informed" consent<br />
! style="font-weight: bold;" | Civic education<br />
! style="font-weight: bold;" | Verification and reliability<br />
! style="font-weight: bold;" | Feedback to data providers (participants)<br />
|-<br />
| How do you go about your crowdsourcing? technologies and techniques<br />
| How do we adress the fact that we don't have a personal relationship with the data subject?<br />
| Has crowdsourced data outlive its time?<br />
| How do you protect crowdsourced data?<br />
| Any legal frameworks that address consent issues?<br />
| What do you do when informed consent might put people in danger?<br />
| Can you transform non-consent data in a way to make it ethically usable?<br />
| How best can we incentivize participation? in data collection<br />
| Do ebola patients have the right to privacy?<br />
| Do we always need consent in data collection?<br />
| How do I have orgs seed sought consent?<br />
| What is an acceptable threshold of "informed" for informed consent to have happened?<br />
| How can orgs augment, with respect to consent, ongoing comms communities already have?<br />
| How to establish reliability of a data source<br />
| Do we (data collectors) have an obligation to make use of the data, to benefit contributors?<br />
|-<br />
| Does technological divide affect crowdsourcing?<br />
| How do you ringfence the use of collected data - open data is a nice idea, but is it ethical?<br />
| Is crowdsourced data viable?<br />
| Any best practice recommended?<br />
| Legal definitions of consent<br />
| Is encryption responsible way to protect users of problems?<br />
| Do we need consent in taking photos in a conference, or open environment?<br />
| <br />
| Is consent relevant when data is anonymized?<br />
| How to move "informed" consent from "all or nothing"<br />
| <br />
| How to think about different forms of consent that is acceptable<br />
| Is civic education a human right?<br />
| What can we learn from cryptographers in verification/anonymity balance?<br />
| Is there an obligation to close the feedback loop once you have crowdsourced info?<br />
|-<br />
| How does mode of communication (interview, phone, SMS) bias the information you get?<br />
| If content is public, does that imply consent?<br />
| Can a source consent to the use of their data if they do not know how it can be used?<br />
| How safe is online data storage?<br />
| What are the legal rules around data/consent?<br />
| What crowdsourcing risks have orgs encountered?<br />
| If you use personal connections to get data that should be public, what are the ethical issues around it?<br />
| <br />
| How do we define responsible?<br />
| What to do when consent is not given, but data is needed?<br />
| <br />
| Is consent required if I take a photo and blur the face?<br />
| Should responsible data sharing be taught in schools?<br />
| How can transparency counteract false/hacked accounts?<br />
| What do you do when there is no help/resource available to participants i.e. no health services in region in W africa for ebola?<br />
|-<br />
| Is there an avenue through which we can track devices for at-risk populations responsibly?<br />
| How do we do consent for bulk/crowdsourced data?<br />
| What do you need to tell people if you are crowdsourcing data, i.e. do you need to tell them it could be used for different purposes?<br />
| How can we ensure responsible data collection through crowdsourcing?<br />
| In absence of laws, how far should we go?<br />
| How can we reduce risk in Disaster in Kenya through responsible data?<br />
| Is consent strictly to prevent harm, or is it broader?<br />
| <br />
| Quoting data sources, especially data that is not public (ex.hospital data)<br />
| Does intended use of data change when we need consent?<br />
| <br />
| How much certainty must you have to know what "informed" consent is?<br />
| How do orgs here define, understand crowdsourcing?<br />
| Can crowdsourced data be extrapolated?<br />
| <br />
|-<br />
| <br />
| How is consent addressed for data mined, say online?<br />
| If you collect data for one purpose can it be used for other purposes?<br />
| After consent, who owns the data, researcher or subject?<br />
| How do we know the legal obligations when collecting data<br />
| How can we make participation less risky?<br />
| Can we compromise consent for the greater good?<br />
| <br />
| <br />
| Must we always have consent to proceed with getting information?<br />
| <br />
| Are there alternative channels of seeking consent?<br />
| How to get collective consent<br />
| How to determine the relevance of crowdsourced data? What is the positional accuracy of crowdsourced data?<br />
| <br />
|-<br />
| <br />
| <br />
| How do I balance and weight sourced data in relation to human rights?<br />
| How do researchers and subjects protect themselves?<br />
| In the absence of data protection laws, what should we use to determine how to proceed?<br />
| How do you access risk/harm if you use data without consent?<br />
| Is it okay to trade personal data to provide a service that is essential to others? i.e. Google<br />
| <br />
| <br />
| What does consent mean in crowdsourcing?<br />
| <br />
| Is there such a thing as implicit informed consent?<br />
| <br />
| Accuracy and integrity of data<br />
| <br />
|-<br />
| <br />
| <br />
| Are orgnaizations and journalists crowdsourcing info?<br />
| <br />
| WHat guides orgs' crowdsourcing activities?<br />
| How have crowdsourcing risks been addressed?<br />
| Is it a researcher's responsibility to protect open data if it could be dangerous?<br />
| <br />
| <br />
| When is verbal consent sufficient?<br />
| <br />
| What is informed consent?<br />
| <br />
| Authenticity<br />
| <br />
|-<br />
| <br />
| <br />
| <br />
| <br />
| Should bloggers be licensed too?<br />
| <br />
| Is it ok to mislead in order to minimize risk?<br />
| <br />
| <br />
| How is consent manifested via implication?<br />
| <br />
| How are subjects informed?<br />
| <br />
| Can we use crowds to verify data?<br />
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|-<br />
| <br />
| <br />
| <br />
| <br />
| Data sourcing policy?<br />
| <br />
| How can we ensure content creators on social media are being responsible/ethical?<br />
| <br />
| <br />
| Are there sets of data where participation itself indicates consent?<br />
| <br />
| Do participants realy understand informed consent? How do you check?<br />
| <br />
| Cloud computing is a trending technology globally. How are we going to enhance security in the cloud?<br />
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|-<br />
| <br />
| <br />
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| What percentage of Kenyans contribute to the legislative process?<br />
| <br />
| Who decides the ethics of data collection?<br />
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|-<br />
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| Do we need consent in layman terms?<br />
| <br />
| How do you ethically balance the need for verifiable information versus anonymity which can provide safety?<br />
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|-<br />
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| Do we have a data protection act in Kenya?<br />
| <br />
| Is ethics subjective and/or geographical?<br />
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| Should journalist be licensed, just like doctors and lawyers?<br />
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| Can I ethically opt out of being a data subject when we need the crowd info to make smart society decisions?<br />
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|}</div>Tinhttps://wiki.responsibledata.io/RDF_Nairobi_consent-ishRDF Nairobi consent-ish2014-10-30T08:51:49Z<p>Tin: Created page with " x800px x800px"</p>
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<div>[[File:Consentish-1.jpg | x800px]]<br />
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[[File:Consentish-2.jpg | x800px]]</div>Tinhttps://wiki.responsibledata.io/RDF_Nairobi_informed_consentRDF Nairobi informed consent2014-10-30T08:51:05Z<p>Tin: Created page with " x800px"</p>
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<div>[[File:Whatisinformedconsent.jpg | x800px]]</div>Tinhttps://wiki.responsibledata.io/Identifying_RiskIdentifying Risk2014-10-30T08:50:12Z<p>Tin: Tin moved page Identifyingrisk to Identifying Risk</p>
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<div>[[File:Nairobi-risk.jpg | x800px]]<br />
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[[Category: RDF Nairobi]]</div>Tinhttps://wiki.responsibledata.io/IdentifyingriskIdentifyingrisk2014-10-30T08:50:12Z<p>Tin: Tin moved page Identifyingrisk to Identifying Risk</p>
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<div>#REDIRECT [[Identifying Risk]]</div>Tin