Data is a Team Sport: Government Priorities and Incentives

Dirk Slater - August 13, 2017 in Ania Calderon, Data Blog, data literacy, Event report, Fabriders, Government, Open Data, research, Tamara Puhovski, Team Sport, The Open Data Charter

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. The conversation in this episode focuses on the challenges of getting governments to prioritise data literacy both externally and internally, and incentives to produce open-data and features:

Ania Calderon, Executive Director at the Open Data Charter, a collaboration between governments and organisations working to open up data based on a shared set of principles. For the past three years, she led the National Open Data Policy in Mexico, delivering a key presidential mandate. She established capacity building programs across more than 200 public institutions.
Tamara Puhovski, a sociologist, innovator, public policy junky and an open government consultant. She describes herself as a time traveler journeying back to 19th and 20th century public policy centers and trying to bring them back to the future.

Notes from the conversation:

Access to government produced open-data is critical for healthy functioning democracies. It takes an eco-system that includes a critical thinking citizenry, knowledgeable civil servants, incentivised elected officials, and smart open-data advocates. Everyone in the eco-system needs to be focused on long-term goals.

Elected officials needs incentivising beyond monetary arguments, as budgetary gains can take a long time to fruition.
Government’s capacities to produce open-data is an issue that needs greater attention.
We need to get past just making arguments for open-data, but be able to provide good solid stories and examples of its benefits.

Resources mentioned in the conversation:

The Google Doc on Open Data: Concerns about opening up data, and responses which have proved effective
From the Open Data Charter and the Transparency and Accountability Initiative: Rethinking data for accountability
A Checklist for Open Government Beginners
From Global Integrity: Exploring How Data Can Make A Difference: A Call for Collective Action
Doing Anti-Corruption Differently: The New ACE Research Programme at SOAS
On the Open Knowledge International Blog: Open data quality – the next shift in open data?
From the GovLab: Why we should care about bad data
White Paper from the WebFoundation: A smart web for a more equal future
Rob Davidson’s Open Data vs Post Fact Politics Presentation

Also, not mentioned, but be sure to check out Tamara’s work on Open Youth

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Data is a Team Sport: Mentors Mediators and Mad Skills

Dirk Slater - August 7, 2017 in advocacy, community, Data Blog, data literacy, Data Maturity, DataKind UK, Emma Prest, Event report, Fabriders, Intermediaries, mentoring, Service Organisations, Team Sport, Tin Geber

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. This episode features:

Emma Prest oversees the running of DataKind UK, leading the community of volunteers and building understanding about what data science can do in the charitable sector. Emma sits on the Editorial Advisory Committee at the Bureau of Investigative Journalism. She was previously a programme coordinator at Tactical Tech, providing hands-on help for activists using data in campaigns.
Tin Geber has been working on the intersection of technology, art and activism for most of the last decade. In his previous role as Design and Tech Lead for The Engine Room, he developed role-playing games for human rights activists; collaborated on augmented reality transmedia projects; and helped NGOs around the world to develop creative ways to combine technology and human rights.

In this episode we take a deep dive into how to get organisations beyond ‘data literacy’ and reach ‘data maturity’, where organisations understand what is good practice on running a data project. Some main points:

A red flag that indicates a data project will end in failure is when the goal is implementation of a tool as opposed to a mission-critical goal.
Training in itself can be helpful with hard skills, such as how to do analysis, but in terms of running data projects, it takes a lot of hand-holding and mentorship is a more effective.
A critical role in and organisations is people who can champion tech and data work, and they need better support in that role.
Fake news and data-driven confusion has meant the need for understanding good data practice is even more important.

DataKind UK’s resources:

Data Maturity Framework
Office hours for non-profits (in person or virtually)
A peer to peer network of non-profit data scientists and analysts – email: emma (at) datakind.org.uk

Tin’s resources:

His GitBook ‘The New Gatekeepers‘

Resources that are inspiring Emma’s Work:

DataKind in the States has a good Data Science 101
Weapons of Math Destruction :
Everybody Lies by Seth Stephens-Davidowitz:

Resources that are inspiring Tin’s work:

DataBasic.io – A a suite of easy-to-use web tools for beginners that introduce concepts of working with data
Media Manipulation and Disinformation Online – Report from Data and Society on how false or misleading information is having real and negative effects on the public consumption of news.
Raw Graphs – The missing link between spreadsheets and data visualization

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Data is a Team Sport: Mentors Mediators and Mad Skills

Dirk Slater - August 7, 2017 in advocacy, community, Data Blog, data literacy, Data Maturity, DataKind UK, Emma Prest, Event report, Fabriders, Intermediaries, mentoring, Service Organisations, Team Sport, Tin Geber

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. This episode features:

Emma Prest oversees the running of DataKind UK, leading the community of volunteers and building understanding about what data science can do in the charitable sector. Emma sits on the Editorial Advisory Committee at the Bureau of Investigative Journalism. She was previously a programme coordinator at Tactical Tech, providing hands-on help for activists using data in campaigns.
Tin Geber has been working on the intersection of technology, art and activism for most of the last decade. In his previous role as Design and Tech Lead for The Engine Room, he developed role-playing games for human rights activists; collaborated on augmented reality transmedia projects; and helped NGOs around the world to develop creative ways to combine technology and human rights.

In this episode we take a deep dive into how to get organisations beyond ‘data literacy’ and reach ‘data maturity’, where organisations understand what is good practice on running a data project. Some main points:

A red flag that indicates a data project will end in failure is when the goal is implementation of a tool as opposed to a mission-critical goal.
Training in itself can be helpful with hard skills, such as how to do analysis, but in terms of running data projects, it takes a lot of hand-holding and mentorship is a more effective.
A critical role in and organisations is people who can champion tech and data work, and they need better support in that role.
Fake news and data-driven confusion has meant the need for understanding good data practice is even more important.

DataKind UK’s resources:

Data Maturity Framework
Office hours for non-profits (in person or virtually)
A peer to peer network of non-profit data scientists and analysts – email: emma (at) datakind.org.uk

Tin’s resources:

His GitBook ‘The New Gatekeepers‘

Resources that are inspiring Emma’s Work:

DataKind in the States has a good Data Science 101
Weapons of Math Destruction :
Everybody Lies by Seth Stephens-Davidowitz:

Resources that are inspiring Tin’s work:

DataBasic.io – A a suite of easy-to-use web tools for beginners that introduce concepts of working with data
Media Manipulation and Disinformation Online – Report from Data and Society on how false or misleading information is having real and negative effects on the public consumption of news.
Raw Graphs – The missing link between spreadsheets and data visualization

View the full online conversation:

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Data is a Team Sport: One on One with Friedhelm Weinberg

Dirk Slater - July 28, 2017 in Capacity Building, Data Blog, documentation, Event report, Fabriders, human rights, research, software development, Team Sport

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher.

Friedhelm Weinberg is the Executive Director of Human Rights Information and Documentation Systems (HURIDOCS), an NGO that supports organisations and individuals to gather, analyse and harness information to promote and protect human rights. In this conversation we take a look at what it takes to be both a tool developer and a capacity builder, and how the two disciplines can inform and build upon each other. Some of the main points:

The capacity building work needs to come first and inform the tool development.
It’s critical that human rights defenders have a clear understanding of what they want to do with the data before they start collecting it.
It’s critical for human rights defenders to have their facts straight as this counts the most in international courts of law, and cuts through ‘fake news.’
Machine learning has enormous potential in documenting human rights abuses in being able to process large amount of case work.
They have been successful in bringing developers in-house by making efforts to get them to better understand how the capacity builders work and also vice-versa.

Specific projects within Huridocs he talked about:

Uwazi is an open-source solution for building and sharing document collections
The Collaboratory is their knowledge sharing network for practitioners focusing on information management and human rights documentation.

Readings/Resources that are inspiring his work:

Reinventing Organisations
Information Architecture, from O’Reilly ,
How Not to Network a Nation, The Uneasy History of the Soviet Internet

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Data is a Team Sport: One on One with Friedhelm Weinberg

Dirk Slater - July 28, 2017 in Capacity Building, Data Blog, documentation, Event report, Fabriders, human rights, research, software development, Team Sport

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher.

Friedhelm Weinberg is the Executive Director of Human Rights Information and Documentation Systems (HURIDOCS), an NGO that supports organisations and individuals to gather, analyse and harness information to promote and protect human rights. In this conversation we take a look at what it takes to be both a tool developer and a capacity builder, and how the two disciplines can inform and build upon each other. Some of the main points:

The capacity building work needs to come first and inform the tool development.
It’s critical that human rights defenders have a clear understanding of what they want to do with the data before they start collecting it.
It’s critical for human rights defenders to have their facts straight as this counts the most in international courts of law, and cuts through ‘fake news.’
Machine learning has enormous potential in documenting human rights abuses in being able to process large amount of case work.
They have been successful in bringing developers in-house by making efforts to get them to better understand how the capacity builders work and also vice-versa.

Specific projects within Huridocs he talked about:

Uwazi is an open-source solution for building and sharing document collections
The Collaboratory is their knowledge sharing network for practitioners focusing on information management and human rights documentation.

Readings/Resources that are inspiring his work:

Reinventing Organisations
Information Architecture, from O’Reilly ,
How Not to Network a Nation, The Uneasy History of the Soviet Internet

View the full online conversation:

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Data is a Team Sport: One on One with Heather Leson

Dirk Slater - July 19, 2017 in community, Data Blog, data literacy, data protection, Event report, Heather Leson, Humanitarian Organisations, IFRC, research, Team Sport

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher.

This episode features a one on one conversation with Heather Leson, the Data Literacy Lead at International Federation of Red Cross and Red Crescent Societies. As a technologist, she strengthens community collaboration via humanitarian technologies and social entrepreneurship. She builds partnerships, curates digital spaces, fosters volunteer engagement and delivers training while inspiring systems for co-creation with maps, code and data. At the International Federation of Red Cross Red Crescent, her mandate includes global data advocacy, data literacy and data training programs in partnership with the 190 national societies and the 13 million volunteers. She is a past Board Member at the Humanitarian OpenStreetMap Team (4 years), Peace Geeks (1 year), and an Advisor for MapSwipe – using gamification systems to crowdsource disaster-based satellite imagery. Previously, she worked as Social Innovation Program Manager, Qatar Computing Research Institute (Qatar Foundation) Director of Community Engagement, Ushahidi, and Community Director, Open Knowledge (School of Data).

Main Points from the Conversation:

Data protection is the default setting for humanitarian organisations collecting data.
She’s found its critical to focus on people and what they are trying to accomplish, as opposed to focusing on tools.
She’s added ‘socialisation’ as the beginning step to the data pipeline.

Heather’s Resources

Blogs/websites

Heather’s work

The full online conversation:

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Data is a Team Sport: Advocacy Organisations

Dirk Slater - July 11, 2017 in Amnesty International, Data Blog, data literacy, Event report, Fabriders, global witness, research, Team Sport

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. In this episode we discussed data driven advocacy organisations with:

Milena Marin is Senior Innovation Campaigner at Amnesty International. She is currently leads Amnesty Decoders – an innovative project aiming to engage digital volunteers in documenting human right violations using new technologies. Previously she worked as programme manager of School of Data. She also worked for over 4 years with Transparency International where she supported TI’s global network to use technology in the fight against corruption.
Sam Leon, is Data Lead at Global Witness, focusing on the use of data to fight corruption and how to turn this information into change making stories. He is currently working with a coalition of data scientists, academics and investigative journalists to build analytical models and tools that enable anti-corruption campaigners to understand and identify corporate networks used for nefarious and corrupt practices.

Notes from the Conversation

In order to get their organisations to see the value and benefit of using data, they both have had to demonstrate results and have looked for opportunities where they could show effective impact. What data does for advocacy is to show the extent of the problem and it provides depths to qualitative and individual stories. Milena credits the work of School of Data for the fact that journalists now expect their to be data accessible from Amnesty to back up their data.

They see gaps in the way that people working in advocacy see data and new technologies as bright shiny and easy answers to their challenges.
In today’s post-fact world, they find that it’s being used to more quickly discredit their work and as a result they need to work harder at presenting verifiable data.
Amnesty’s decoder project has involved 45,000 volunteers and along with being able to review a huge amount of video, they have also gotten training and a deeper understanding of what Amnesty does.
Global Witness has had a limited amount of data-sets they have released to the public and would like to be releasing more data that can be of use to communities. However there is a long way to go before their data can be open by default as there needs to be further learning and understanding of how data can make individuals more vulnerable.
Advocacy organisations need to use intermediaries and externals to cover the gaps in their own expertise around data.

More about their work

Milena

Amnesty Decoders Project

Sam

Dirk

What we’ve learned about creating podcasts

Resources and Readings

Dear Data, a year-long, analog data drawing project by Giorgia Lupi and Stefanie Posavec. Extremely inspiring work!
Buzzfeed Open Labs presentation on Mining the Social Web
Precision Journalism: A Reporter’s Introduction to Social Science Methods –
The NICAR List
DataKind UK’s data maturity model –

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Data is a Team Sport: One on One with Daniela Lepiz

Dirk Slater - July 3, 2017 in community, Data Blog, Data Journalism, data literacy, Event report, Fabriders, Nigeria, research, Team Sport, West Africa

Data is a Team Sport is our open-research project exploring the data literacy eco-system and how it is evolving in the wake of post-fact, fake news and data-driven confusion. We are producing a series of videos, blog posts and podcasts based on a series of online conversations we are having with data literacy practitioners. To subscribe to the podcast series, cut and paste the following link into your podcast manager : http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher.

This episode features a one on one episode with Daniela Lepiz, a Costa Rican data journalist and trainer, who is currently the Investigation Editor for CENOZO, a West African Investigative Journalism Project that aims to promote and support cross border data investigation and open data in the region. She has a masters degree in data journalism from the Rey Juan Carlos University in Madrid, Spain. Previously involved with OpenUP South Africa working with journalists to produce data driven stories. Daniela is also a trainer for the Tanzania Media Foundation and has been involved in many other projects with South African Media, La Nacion in Costa Rica and other international organisations.

Notes from the conversation

Daniela spoke to us from Burkina Faso and reflected on the role of journalism and particularly data-driven journalism in functioning democracies. The project she is working on empowering journalists working cross-border in western Africa to utilise data to expose corruption and violation of human rights. To identify journalists to participate in the project, they have looked for individuals who are experienced, passionate and curious. The project engages existing media houses, such as Premium Times in Nigeria, to assure that there are places for their stories to appear. Important points Daniela raises:

Media is continually evolving and learning to evolve and Daniela can see that data literacy will be a required proficiency in the next five years.
The biggest barrier to achieving open-data in government are government officials who resist transparency
There is a real fear from journalists of having to be proficient in maths when they are considering improve their skills to produce data-driven stories. They often fail to realise that its about working with others that have skills on statistics and data analysis.
Trust in media has declined in such a big way and it means journalists have to work that much harder, particularly in labelling things as opinion or being biased.

Resources she finds inspiring

Her blogs posts

The full online conversation:

Daniela’s bookmarks!

These are the resources she uses the most often. .Rddj – Resources for doing data journalism with R Comparing Columns in Google Refine | OUseful.Info, the blog…Journalist datastores: where can you find them? A list. | Simon Rogers AidInfoPlus – Mastering Aid Information for Change

Data skills

Mapping tip: how to convert and filter KML into a list with Open Refine | Online Journalism Blog
Mapbox + Weather Data
Encryption, Journalism and Free Expression | The Mozilla Blog
Data cleaning with Regular Expressions (NICAR) – Google Docs
NICAR 2016 Links and Tips – Google Docs
Teaching Data Journalism: A Survey & Model Curricula | Global Investigative Journalism Network
Data bulletproofing tips for NICAR 2016 – Google Docs
Using the command line tabula extractor tool · tabulapdf/tabula-extractor Wiki · GitHub
Talend Downloads
Github Git Concepts – SmartGit (Latest/Preview) – Confluence GitHub For Beginners: Don’t Get Scared, Get Started – ReadWrite Kartograph.org
LittleSis – Profiling the powers that be
Tableau customized polygons How can I create a filled map with custom polygons in Tableau given point data? – Stack Overflow Using Shape Files for Boundaries in Tableau | The Last Data Bender How to make custom Tableau maps How to map geographies in Tableau that are not built in to the product (e.g. UK postcodes, sales areas) – Dabbling with Data Alteryx Analytics Gallery | Public Gallery TableauShapeMaker – Adding custom shapes to Tableau maps | Vishful thinking… Creating Tableau Polygons from ArcGIS Shapefiles | Tableau Software Creating Polygon-Shaded Maps | Tableau Software Tool to Convert ArcGIS Shapefiles into Tableau Polygons | Tableau and Behold! Polygon Maps | Tableau Software
Modeling April 2016
5 Tips for Making Your Tableau Public Viz Go Viral | Tableau Public
Google News Lab
HTML and CSS
Open Semantic Search: Your own search engine for documents, images, tables, files, intranet & news
Spatial Data Download | DIVA-GIS
Linkurious – Linkurious – Understand the connections in your data
Apache Solr –
Apache Tika – Apache Tika
Neo4j Graph Database: Unlock the Value of Data Relationships
SQL: Table Transformation | Codecademy
dc.js – Dimensional Charting Javascript Library
The People and the Technology Behind the Panama Papers | Global Investigative Journalism Network
How to convert XLS file to CSV in Command Line [Linux]
Intro to SQL (IRE 2016) · GitHub
Malik Singleton – SELECT needle FROM haystack;
Investigative Reporters and Editors | Tipsheets and links
Investigative Reporters and Editors | Tipsheets and Links
SQL_PYTHON More data 2016-NICAR-Adv-SQL/SQL_queries.md at master · taggartk/2016-NICAR-Adv-SQL · GitHub advanced-sql-nicar15/stats-functions.sql at master · anthonydb/advanced-sql-nicar15 · GitHub 2016-NICAR-Adv-SQL/SQL_queries.md at master · taggartk/2016-NICAR-Adv-SQL · GitHub Malik Singleton – SELECT needle FROM haystack; Statistical functions in MySQL • Code is poetry Data Analysis Using SQL and Excel – Gordon S. Linoff – Google Books Using PROC SQL to Find Uncommon Observations Between 2 Data Sets in SAS | The Chemical Statistician mysql – Query to compare two subsets of data from the same table? – Database Administrators Stack Exchange sql – How to add “weights” to a MySQL table and select random values according to these? – Stack Overflow sql – Fast mysql random weighted choice on big database – Stack Overflow php – MySQL: Select Random Entry, but Weight Towards Certain Entries – Stack Overflow MySQL Moving average Calculating descriptive statistics in MySQL | codediesel Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, … R, MySQL, LM and quantreg 26318_AllText_Print.pdf ddi-documentation-english-572 (1).pdf Categorical Data — pandas 0.18.1+143.g3b75e03.dirty documentation python – Loading STATA file: Categorial values must be unique – Stack Overflow Using the CSV module in Python 14.1. csv — CSV File Reading and Writing — Python 3.5.2rc1 documentation csvsql — csvkit 0.9.1 documentation weight samples with python – Google Search python – Weighted choice short and simple – Stack Overflow 7.1. string — Common string operations — Python v2.6.9 documentation Introduction to Data Analysis with Python | Lynda.com A Complete Tutorial to Learn Data Science with Python from Scratch GitHub – fonnesbeck/statistical-analysis-python-tutorial: Statistical Data Analysis in Python
Verifying the email – Email Checker
A little tour of aleph, a data search tool for reporters – pudo.org (Friedrich Lindenberg)
Welcome – Investigative Dashboard Search
Investigative Dashboard
Working with CSVs on the Command Line
FiveThirtyEight’s data journalism workflow with R | useR! 2016 international R User conference | Channel 9
Six issue when installing package · Issue #3165 · pypa/pip · GitHub
python – Installing pip on Mac OS X – Stack Overflow
Source – Journalism Code, Context & Community – A project by Knight-Mozilla OpenNews
Introducing Kaggle’s Open Data Platform
NASA just made all the scientific research it funds available for free – ScienceAlert
District council code list | Statistics South Africa
How-to: Index Scanned PDFs at Scale Using Fewer Than 50 Lines of Code – Cloudera Engineering Blog
GitHub – gavinr/geojson-csv-join: A script to take a GeoJSON file, and JOIN data onto that file from a CSV file.
7 command-line tools for data science
Python Basics: Lists, Dictionaries, & Booleans
Jupyter Notebook Viewer
PYTHON FOR JOURNALISTS New folder Reshaping and Pivot Tables — pandas 0.18.1 documentation Reshaping in Pandas – Pivot, Pivot-Table, Stack and Unstack explained with Pictures – Nikolay Grozev Pandas Pivot-Table Example – YouTube pandas.pivot_table — pandas 0.18.1 documentation Pandas Pivot Table Explained – Practical Business Python Pivot Tables In Pandas – Python Pandas .groupby(), Lambda Functions, & Pivot Tables Counting Values & Basic Plotting in Python Creating Pandas DataFrames & Selecting Data Filtering Data in Python with Boolean Indexes Deriving New Columns & Defining Python Functions Python Histograms, Box Plots, & Distributions Resources for Further Learning Python Methods, Functions, & Libraries Python Basics: Lists, Dictionaries, & Booleans Real-world Python for data-crunching journalists | TrendCT Cookbook — agate 1.4.0 documentation 3. Power tools — csvkit 0.9.1 documentation Tutorial — csvkit 0.9.1 documentation 4. Going elsewhere with your data — csvkit 0.9.1 documentation 2. Examining the data — csvkit 0.9.1 documentation A Complete Tutorial to Learn Data Science with Python from Scratch For Journalism
ProPublica Summer Data Institute
Percentage of vote change | CARTO
Data Science | Coursera
Data journalism training materials
Pythex: a Python regular expression editor
A secure whistleblowing platform for African media | afriLEAKS
PDFUnlock! – Unlock secured PDF files online for free.
The digital journalist’s toolbox: mapping | IJNet
Bulletproof Data Journalism – Course – LEARNO
Transpose columns across rows (grefine 2.5) ~ RefinePro Knowledge Base for OpenRefine
Installing NLTK — NLTK 3.0 documentation
1. Language Processing and Python
Visualize any Text as a Network – Textexture
10 tools that can help data journalists do better work, be more efficient – Poynter
Workshop Attendance
Clustering In Depth · OpenRefine/OpenRefine Wiki · GitHub
Regression analysis using Python
DataBasic.io
DataBasic.io
R for Every Survey Analysis – YouTube
Git – Book
NICAR17 Slides, Links & Tutorials #NICAR17 // Ricochet by Chrys Wu
Register for Anonymous VPN Services | PIA Services
The Bureau of Investigative Journalism
dtSearch – Text Retrieval / Full Text Search Engine
Investigation, Cybersecurity, Information Governance and eDiscovery Software | Nuix
How we built the Offshore Leaks Database | International Consortium of Investigative Journalists
Liz Telecom/Azimmo – Google Search
First Python Notebook — First Python Notebook 1.0 documentation
GitHub – JasonKessler/scattertext: Beautiful visualizations of how language differs among document types

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Data is a Team Sport: Data-Driven Journalism

Dirk Slater - June 20, 2017 in Anti-corruption, community, Data Blog, data driven journalism, Data Journalism, data literacy, Event report, Fabriders, Gender Data, research, Rights

Our podcast series that explores the ever evolving data literacy eco-system. Cut and paste this link into your podcast app to subscribe: http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. In this episode we speak with two veteran data literacy practitioners who have been involved with developing data-driven journalism teams. Our guests:

Eva Constantaras is a data journalist specialized in building data journalism teams in developing countries. These teams that have reported from across Latin America, Asia and East Africa on topics ranging from displacement and kidnapping by organized crime networks to extractive industries and public health. As a Google Data Journalism Scholar and a Fulbright Fellow, she developed a course for investigative and data journalism in high-risk environments.
Natalia Mazotte is Program Manager of School of Data in Brazil and founder and co-director of the digital magazine Gender and Number. She has a Master Degree in Communications and Culture from the Federal University of Rio de Janeiro and a specialization in Digital Strategy from Pompeu Fabra University (Barcelona/Spain). Natalia has been teaching data skills in different universities and newsrooms around Brazil. She also works as instructor in online courses in the Knight Center for Journalism in the Americas, a project from Texas University, and writes for international publications such as SGI News, Bertelsmann-Stiftung, Euroactiv and Nieman Lab.

Notes from this episode

They both describe the lessons learned in getting journalists to use data that can drive social change. For Eva, getting journalists to work harder and just reporting that corruption exists is not enough, while Natalia, talks about how they use data on gender to drive debate and discussion around equality. What is critical for democracy is the existence of good journalism and this includes data-driven journalism that uncovers facts and gets at the root causes.

Gaps in the Data Literacy EcoSystem:

Natalia points out that corporations and government has the power because they are data-literate and can use it effectively, while people in low-income communities, such as favela’s really suffer because they are at the mercy of what story gets told by looking at the ‘official’ data. Eva feels that there has been too much emphasis on short-term and quick solutions from individuals who have put a lot of money in making sure that data is ready and accessible. Donors need to support more long-term efforts and engagement around data-literacy.

Adjusting to a ‘post-fact’ world means:

Western journalists have spent too much time focusing on reporting on polling data rather than reporting on policies and it’s important for newer journalists to understand why that was problematic. In Brazil, the main stream media is focusing on ‘what’s happened’ while independent media is focusing on ‘why it’s happened’ and this means the media landscape is changing.

They also talked about:

Ethics and the responsibility inherent in gathering and storing data, along with the grey areas around privacy.
How to get media outlets to value data-driven journalism by getting them to understand that people are increasingly getting their ‘breaking news’ from social media, so they need to look at providing more in-depth stories.

They wanted to plug:

Eva: UNDP summer data journalism institute
Natalia: Gender and Number

Readings/Resources they find inspiring for their work.

Resources contributed from the participants:

View the online conversation in full:

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Data is a Team Sport: Data-Driven Journalism

Dirk Slater - June 20, 2017 in Anti-corruption, community, Data Blog, data driven journalism, Data Journalism, data literacy, Event report, Fabriders, Gender Data, research, Rights

Our podcast series that explores the ever evolving data literacy eco-system. Cut and paste this link into your podcast app to subscribe: http://feeds.soundcloud.com/users/soundcloud:users:311573348/sounds.rss or find us in the iTunes Store and Stitcher. In this episode we speak with two veteran data literacy practitioners who have been involved with developing data-driven journalism teams. Our guests:

Eva Constantaras is a data journalist specialized in building data journalism teams in developing countries. These teams that have reported from across Latin America, Asia and East Africa on topics ranging from displacement and kidnapping by organized crime networks to extractive industries and public health. As a Google Data Journalism Scholar and a Fulbright Fellow, she developed a course for investigative and data journalism in high-risk environments.
Natalia Mazotte is Program Manager of School of Data in Brazil and founder and co-director of the digital magazine Gender and Number. She has a Master Degree in Communications and Culture from the Federal University of Rio de Janeiro and a specialization in Digital Strategy from Pompeu Fabra University (Barcelona/Spain). Natalia has been teaching data skills in different universities and newsrooms around Brazil. She also works as instructor in online courses in the Knight Center for Journalism in the Americas, a project from Texas University, and writes for international publications such as SGI News, Bertelsmann-Stiftung, Euroactiv and Nieman Lab.

Notes from this episode

They both describe the lessons learned in getting journalists to use data that can drive social change. For Eva, getting journalists to work harder and just reporting that corruption exists is not enough, while Natalia, talks about how they use data on gender to drive debate and discussion around equality. What is critical for democracy is the existence of good journalism and this includes data-driven journalism that uncovers facts and gets at the root causes.

Gaps in the Data Literacy EcoSystem:

Natalia points out that corporations and government has the power because they are data-literate and can use it effectively, while people in low-income communities, such as favela’s really suffer because they are at the mercy of what story gets told by looking at the ‘official’ data. Eva feels that there has been too much emphasis on short-term and quick solutions from individuals who have put a lot of money in making sure that data is ready and accessible. Donors need to support more long-term efforts and engagement around data-literacy.

Adjusting to a ‘post-fact’ world means:

Western journalists have spent too much time focusing on reporting on polling data rather than reporting on policies and it’s important for newer journalists to understand why that was problematic. In Brazil, the main stream media is focusing on ‘what’s happened’ while independent media is focusing on ‘why it’s happened’ and this means the media landscape is changing.

They also talked about:

Ethics and the responsibility inherent in gathering and storing data, along with the grey areas around privacy.
How to get media outlets to value data-driven journalism by getting them to understand that people are increasingly getting their ‘breaking news’ from social media, so they need to look at providing more in-depth stories.

They wanted to plug:

Eva: UNDP summer data journalism institute
Natalia: Gender and Number

Readings/Resources they find inspiring for their work.

Resources contributed from the participants:

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Notes from this episode

Gaps in the Data Literacy EcoSystem:

Adjusting to a ‘post-fact’ world means:

They also talked about:

They wanted to plug:

Readings/Resources they find inspiring for their work.

Resources contributed from the participants:

View the online conversation in full:

Notes from this episode

Gaps in the Data Literacy EcoSystem:

Adjusting to a ‘post-fact’ world means:

They also talked about:

They wanted to plug:

Readings/Resources they find inspiring for their work.

Resources contributed from the participants:

View the online conversation in full:

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