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Data is a Team Sport: Mentors Mediators and Mad Skills

Dirk Slater - August 7, 2017 in Community, Data Blog, Event report

Data is a Team Sport is a series of online conversations held with data literacy practitioners in mid-2017 that explores the ever evolving data literacy eco-system.

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.

Notes from the conversation

In this episode we discussed ways to move organisations beyond data literacy and to the point of data maturity, where organisations are able to manage data-driven projects on their own. Training in itself can be helpful with hard skills, such as how to do analysis, but in terms of learning how to run a data projects, Emma asserts that you have to run a project with them as it takes a lot of hand-holding. There needs to be commitment within the entire organisation to implement a data project, as it will take support and inputs from all parts.  The goal of DataKind UK’s long-term engagements is to help an organisation to build an understanding of what is good data practice.  

Tin points out how critical it is for organisations to be able to learn from others that are working in similar contexts and environments. While there are international networks and resources that are accessible, his biggest challenge is identifying local networks that his clients can connect with and receive peer support.

Another critical element for reaching data maturity, is the existence of champions striving to develop good data practice within an organisation. Tin and Emma both acknowledge that these types of individuals are rare, have a unique skill set, and are often not in senior management positions. There’s a need for greater support for these individuals in the form of: mentoring, networks of practice and training courses that focus on how other organisations have successfully run data projects.

Intermediaries are often focused on demystifying new technologies for civil society organisations. Currently a lot of emphasis on grappling with the implications of machine learning, but it tends to point out the negative impacts (i.e. Cathy O’Neil’s book on ‘Weapons of Math Destruction’), and there needs to be greater examination of positive impacts and stories of CSO’s using it well and contributing to social good.

DataKind UK’s resources:

Tin’s resources:

Resources that are inspiring Emma’s Work:

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 Heather Leson

Dirk Slater - July 19, 2017 in Community, Data Blog, Event report

Data is a Team Sport is a series of online conversations held with data literacy practitioners in mid-2017 that explores the ever evolving data literacy eco-system.

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 where 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).

Notes from the Conversation:

Heather talked about the need for Humanitarian organisations to lead their data projects with a ‘do no harm’ approach, and how keeping data and individual information they collect safe was paramount. During her first 10 months developing a data literacy program for the Federation, she focused on identifying internal expertise and providing opportunities for peer exchange. She has relied heavily on external knowledge, expertise and resources that have been shared amongst data literacy practitioners through participating in networks and communities such as School of Data.

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 12, 2017 in Community, Data Blog, Event report

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. Advocates are often quick to see data and new technologies as easy answers to their challenges, yet have difficulty in foreseeing the realities of implementing complex projects that utilise them.

Data provides advocates with ways to reveal the extent of a problem and  provide depth to qualitative and individual stories.  Milena credits the work of School of Data for the fact that journalists now expect Amnesty to back up their stories with data. However, the term ‘fake news’ is used to discredit their work and as a result they work harder at presenting verifiable data.

Data projects also can provide additional benefit to advocacy organisations by engaging stakeholders. Amnesty’s decoder project has involved 45,000 volunteers, and along with being able to extract data from a huge amount of video, it has also provided those volunteers with a deeper understanding of Amnesty’s work.  Global Witness is striving to make their data publicly accessible so it can provide benefit to their allies. Global Witness acknowledges that they are still are learning about ethical and privacy considerations before open data-sets can be a default. Both organisations are actively learning

They also touched on how important it is for their organisations to learn from others. They  look to external consultants and intermediaries to help fill organisational gaps in expertise in using data. They find it critical for organisations like Open Knowledge and School of Data to convene practitioners from different disciplines to share methodologies and lessons learned. During the conversation, they offered to share their own internal curriculums with each other.

More about their work

Milena

Sam

Resources and Readings

From FabRiders

View the Full Conversation:

 

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

Dirk Slater - July 3, 2017 in Community, Data Blog, Event report

Data is a Team Sport is a series of online conversations held with data literacy practitioners in mid-2017 that explores the ever evolving data literacy eco-system.

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 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 importance of data-driven journalism in holding power to accountability. Her project aims to train and support  journalists working across borders in West Africa to use data to expose corruption and human rights violation. To identify journalists to participate in the project, they seek individuals who are experienced, passionate and curious. The project engages media houses, such as Premium Times in Nigeria, to ensure that there are respected outlets to publish their stories. Daniela raised the following points:

  • As the media landscape continues to evolve, data literacy is increasing becoming a required competency
  • Journalists do not necessarily have a background in mathematics or statistics and are often intimidated by the idea of having to these concepts in their stories.
  • Data stories are best done in teams of people with complementary skills. This can go against a traditional approach to journalism in which journalists work alone and tightly guard their sources.
  • It is important that data training programmes also work with, and better understand the needs of journalists.

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 RComparing Columns in Google Refine | OUseful.Info, the blog…Journalist datastores: where can you find them? A list. | Simon RogersAidInfoPlus – 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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Announcing our new member: ‘Caribbean School of Data’

Meg Foulkes - June 21, 2017 in Announcement, Community

Today we’re delighted to welcome a new organisational member to our network: the Caribbean Open Institute! They will carry the Caribbean School of Data Initiative.

The new Caribbean initiative is led by Maurice McNaughton who coordinates the Caribbean Open Institute, as the regional node for the Open Data for Development network activities in the Caribbean. The COI coalition of partner organisations and individuals conduct regional open data research, advocacy, and capacity-building activities such as the Global Open Data Index and the Open Data Barometer. The new “Caribbean School of Data” will be hosted at the Mona School of Business & Management, UWI and affiliate institutions are planned for other countries across the Caribbean (including Trinidad & Tobago,  Haiti, Cuba and Guyana).

Already in the group’s pipeline is a virtual incubation model to encourage and facilitate data-driven entrepreneurial startups as well as a project to build a Caribbean data competency map, to identify and make searchable and accessible, individual and institutional clusters of data skills, knowledge and capabilities in the region.

School of Data is already working with the Caribbean Open Institute on a Data literacy project in Haïti called “Going Global: Digital Jobs and Gender” for which we have recently recruited two Fellows.

Welcome, Caribbean School of Data!

 

About School of Data members

School of Data’s organisational members are legally independent groups, affiliated formally through a memorandum of understanding. Our members are groups whose mission and activities are aligned with ours and with whom we plan to collaborate in this data literacy work. Caribbean School of Data  is our fourteenth member!

 

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Data is a Team Sport: Data-Driven Journalism

Dirk Slater - June 20, 2017 in Community, Data Blog, Event report

Data is a Team Sport is a series of online conversations held with data literacy practitioners in mid-2017 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

Our first conversation on Data-Driven Journalism featured Eva Constantaras, on her work in developing data-driven journalism teams in Afghanistan and Pakistan, and Natalia Mazotte on her work in Brazil. They discussed what they have learned helping journalists think through how they can use data to drive social change. They agreed that good journalism necessarily includes data-driven approaches in order uncover facts and the root causes of societal problems.

Eva strives to motivate journalists to look beyond the fact that corruption exists and dig deeper into the causes and impacts. She has seen data journalists in Europe and North America making a choice to focus, for example, on polling data rather than breaking down the data behind the candidates’ policies. Eva sees this as a mistake and is committed to making emerging data journalists understand why this is problematic. Finally, Eva made a critique of the approach funders take in the field of data literacy, often putting too much emphasis on short-term solution rather than investing in long-term data capacity building programmes. This is something that School of Data has long struggled with from third-party funders and clients alike. It’s clear that more work needs to be done to explaining what short term programmes can and, more importantly, cannot achieve.

Natalia primarily discussed School of Data Brazil’s Gender and Number project. The project was designed to use data to move the discussion on gender equality past arguments based on traditional roles. She is concerned about the growing ‘data literacy’ gap between those with power, government and corporations, and those without power, people living in the favelas. In Brazil, the media landscape is changing as the mainstream are reporting  on ‘what’s happened’ while independent media is doing the more investigative reporting on ‘why it’s happened’.

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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Data is a Team Sport: Enabling Learning

Dirk Slater - June 6, 2017 in Community, Event report

Data is a Team Sport is a series of online conversations held with data literacy practitioners in mid-2017 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.

In this episode we speak with two veteran data literacy practitioners who have been involved with directly engaging learners to get beyond spreadsheets to build confidence and take agency in their own learning.

Our guests:

  • Rahul Bhargava is a researcher and technologist specializing in civic technology and data literacy. He creates interactive websites used by hundreds of thousands, playful educational experiences across the globe, and award-winning visualizations for museum settings. As a research scientist at the MIT Center for Civic Media, Rahul leads technical development on projects ranging from interfaces for quantitative news analysis to platforms for crowd-sourced sensing.
  • Lucy Chambers initially embarked on a career as a journalist, she took a few turns which lead to a career at Open Knowledge teaching journalists how and why to work with data. She was one of the editors of the Data Journalism Handbook. She later lead the highly successful School of Data programme which extended technical training to non-profit organisations. Lately, she has focussed on delivery of software projects as a product manager. Most recently, she has been working in West Africa on health related software.

Notes from this episode

Rahul talked to us about how his experience in leading workshops and data capacity building programmes led to the creation of DataBasics.io while Lucy discussed the rationale behind School of Data shifting its focus from online curriculum to fellowships.

In order for his participants to begin to understand how to work with data, Rahul asks them to  create drawings about what a dataset tells them. He then asks the participants create a gallery of their drawings and encourages them to critically assess each other’s work.. This process of having workshop participants create and examine data visualisations led to the creation of databasic.io, a set of tools specifically designed allow users to visualise datasets in a number of different ways.

Lucy, recalled School of Data’s initial struggles developing an online curriculum that attempted to provide ‘how-to’s’ on a diverse set of data tools. Eventually, through focus groups and testing of the curriculum, School of Data began to understand that this tool centred approach was unlikely to effectively serve School of Data’s target audience. As a result, the team shifted their strategy and began to focus their attention on developing the capacity of future data trainers. This ultimately lead School of Data to create the Fellowship Programme, a programmed designed to take high potential individuals and build their capacity to be data literacy trainers and leaders.

Finally, both shared their thoughts on building data literacy at the organisational level. They agreed that a common mistake made by organisations is thinking about data use as being exclusively part of the IT function of the organisation. Rather, when they both look at ways to build an active data culture within organisations, where staff converse about and engage with data, rather than take a passive approach such as producing dashboards.

They wanted to plug:

  • Rahul is building a co-hort around further development of databasics.io. Ping him via twitter to get more information on that.
  • Lucy’s blog is Tech to Human and she writes about her work and what she’s learning. She is working on a project for MySociety called EveryPolitician and writing about it on Medium.

Readings/Resources they find inspiring for data literacy work.

View the full online conversations:

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How do you become data literate? Part 3

Meg Foulkes - May 31, 2017 in Community

What does it mean to become ‘data literate’? Where do you start and how can you use data within your work and projects? To explore these questions, we would like to introduce some of our community members and data activists from around the world, who ended up working with data at some point in their lives. We were curious about how they actually got started and – looking back now – what they would recommend to data newbies.

Each month we will publish a new interview, this is no. #3. Got feedback? Have questions? Feel free to get in touch: helene.hahn@okfn.de

 

Vadym Hudyma

Social change activist, data trainer at School of Data, from Kiev, Ukraine

Topics: responsible data, data security

Tweets: @VadymGud

 

Introduce yourself.

My name is Vadym from Kiev, Ukraine. I’m a School of Data fellow working on responsible data and privacy. I worked with different Ukraine-based NGOs and helped them to design their data pipeline and their data projects in a more secure and responsible way. I’d like to give them an understanding that data sometimes can not only be liberating, but also harmful. I’m trying to persuade NGOs to consider this issue in their internal processes. Also I’m doing independent research on how our government is using personal data, how it is being stored and reused. I’m also following data discussions on the current Ukrainian situation, because we have a very huge and vibrant open data community.

 

When was the first time you came across data and when did you start to use data in your work?

I started interacting with data as a website editor. I worked on a very interesting project on monitoring MP’s activities and at some point our partners, who supposed to be doing analysis, failed miserably doing their job. We had two weeks left to finish the project before the launch. I have looked through the results gathered so far with my journalism background and I sensed that something terribly wrong could be published. So I gather some data-savvy friends, who could quickly gather the data needed and analyze what was really going on. We managed to finish our project in time. So this was the first stress test on data analysis and data cleaning. After this I started working with government data and slowly discovered other datasets for me.

In Ukraine, between 2012 and 2013, NGOs were under much pressure by the government. Many have been investigated, we were monitored all the time. It’s still the case now, but much less dangerous than it used to be. Within the NGO I worked with, I took care of digital security. I was trying to secure our communication and tried to implement basic understanding of digital security within the organisation.

Basically, it was when I begin to understand both the importance of data usage for putting pressure on the government and why it’s important to protect your digital infrastructure.

 

What topics and projects are you currently working on?

There is one very progressive governmental agency working in education. They recently published datasets on educational data, but it wasn’t properly anonymised. There are some possibilities to de-anonymise thousands of people and their educational records, which is a very sensitive information on different levels. So I was working on an article about this issue and I’m in contact with the agency to solve the issue on the public personal data. There needs to be a better understand of how data can be used and published securely. Having data available opens up many possibilities, but we should be aware of the risks as well.

 

How would you explain data literacy, what does it mean to you?

We live in a society that hugely depends on data, whether we understand it or not. In the last years we were dealing mostly with data held by governments. But now we have much more access to data and the whole process was further democratized in a way. This availability brings possibilities as well as risks. For me, data literacy is the ability of people to think about data not as a neutral tool, but as something that could shape society in a way, that we initially may not even expect. We should really be careful and thoughtful, when designing data project, doing research and investigations. We need to think not just about our audience, who reads our data stories or uses our data sets, but also about the people behind those data sets. So data literacy for me is understanding that there is not just numbers or data points, but also real people behind those.

 

What would you recommend to someone interested in data, but does not know where to start?

Probably I would recommend defining your goals. It helps to know, what you want to achieve and then look around for information and data sets available online.

 

Links:

Blog posts by Vadym on School of Data

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How do you become data literate? Part 1

helene.hahn - March 9, 2017 in Community, Data Blog

We at Open Knowledge Foundation Germany launched a new project this year, that we’re very proud of: Datenschule (datenschule.de), the German version of School of Data. We want to encourage civil society organisations, journalists and human rights defenders to use data and technology effectively within their work to create positive social change.

But what does it actually mean to become ‘data literate’? Where do you start and how can you use data within your work and projects? To explore these questions, we would like to introduce some of our community members and data activists from around the world, who ended up working with data at some point in their lives. We were curious about how they actually got started and – looking back now – what they would recommend to data newbies.

Each month we will publish a new interview, this is no. #1. Got feedback? Have questions? Feel free to get in touch: helene.hahn@okfn.de

 

Camila Salazar

 

 

 

Who: Data-Journalist from Costa Rica, working at the newsroom La Nación, data trainer at School of Data

Topics: data-driven stories on society, economics, politics

Tweets: @milamila07

 

Hi Camila, please introduce yourself.

My name is Camila, I’m from Costa Rica and I’m a data journalist and an economist. I’m currently working at a newspaper called La Nación in the data unit. I’m also involved with the School of Data community and started as a fellow in 2015. This was the year when I started running data trainings and workshops. I was trying to build a community around data in Costa Rica, in Latin America, also a bit in Mexico and South America.

When was the first time you came across data and when did you start to use data in your work?

I started studying journalism, but after my second year I was disappointed with the university and I wasn’t really motivated. So I thought, maybe I could start studying something else besides journalism. I enrolled myself in Economics at my university and was taking two courses simultaneously. In Economics it’s all about numbers and I really liked it. But when I was about to finish journalism studies, I thought, do I want to be an economist and work in a bank or do I want to write stories? How could I combine both? That’s how I got involved in data journalism. I found that this was an area where you could combine both in a good way. So you can take all the methods and technical skills, that you acquire in an economics degree, and apply them to tell stories of public-interest, so that’s how I mix the both and so far, it’s worked well.

What topics and projects are you currently working on?

At the data unit at La Nación we don’t focus on one major topic, it differs all time. This year we ran projects about the municipal elections in Costa Rica. We collected data regarding the mayors, that were running for the different counties. We also developed a project about live fact-checking the promises of the president. Every year, he gives a speech about the situation in the country. We built a platform where you could follow the speech live and see, if the things that the president saysare true or not. We tried to look for all kind of stories and narratives and see what kind of data is available on that topic. It could be a social topic, an economic one or something else. Now we are working on a project around wages. Within our unit we had the liberty to choose our topics and to see what’s interesting.

How would you explain data literacy?

I think to be data literate is to change the way you solve problems. You don’t have to be super pro in statistics. It’s a way you approach questions and the way you solve them. So for example, if you’re working in a social discipline, in economics or in science you are used to solve problems with certain scientific methods, you ask a questions, apply a method and then try to prove your point, you experiment a lot with data. That’s the way you become data literate. And this can work in any kind of field, in data journalism, public policy, in economics, if you are trying to introduce better solutions to improve efficiency in your business. Data literacy is about changing your way of thinking. It’s about trying to prove things and trying to find solutions with numbers and data. It’s a way of making things more methodical and reproducible.

What would you recommend to someone interested in data, but who does not know where to start?

If you really don’t know anything about data, don’t worry, it’s not that hard to get started. There are many learning resources available online. For a start, I would try and look for projects of people who already work with data – to get inspired. Then you can look for tools online, for example, on schoolofdata.org, there are courses, there are links to projects and it’s a good way to start. Don’t be afraid, and if you want to go super pro, I encourage you to do this. But it’s a process, you don’t need to expect to be modelling data in two weeks, but in two weeks you can learn the basics and start answering small questions with data.

Links:

Blog posts by Camila on School of Data

Data unit at the newspaper La Nación

Live fact-checking project on presidential promises http://www.nacion.com/gnfactory/investigacion/2016/promesas-presidente/index.html

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Welcome to School of Data’s Second Steering Committee!

Cedric Lombion - October 11, 2016 in Announcement, Community

During the 2016 School of Data Summer Camp, a new Steering Committee has been elected by our members. Replacing the “transition” Steering Committee, which oversaw the transformation of School of Data into a network-driven project, the new Steering Committee is elected for 2 years, as will be future ones. Along with overseeing the budget, strategy and sustainability of the School of Data project, the new Steering Committee will oversee the formation incorporation of School of Data into a dedicated NGO.

Welcome to the new Steering Committee!

Bardhyl Jashari

Bardhyl is the director of Metamorphosis Foundation (Macedonia). His professional interests are mainly in the sphere of new technologies, media, civic activism, e-government and participation. Previously he worked as Information Program Coordinator of the Foundation Open Society – Macedonia. He is a member of the National Council for Information Society of Macedonia and National Expert for Macedonia of the UN World Summit Award.

Pavel Richter

Pavel is Chief Executive Officer at Open Knowledge International. He was Executive Director of Wikimedia Deutschland, and pioneered the internationally acclaimed Wikidata project which is now the fastest growing project for open structured data. Pavel is in on the Advisory Board of Transparency International Germany and Code for Germany. He holds a Masters Degree in Political Science, History and Constitutional Law. He worked for 12 years as a management consultant in the IT and banking industry, before he started to focus on managing non-profit organisations.

Camila Salazar

Camila is a journalist, economist and data journalism professor currently working with the data unit of the La Nacion Costa Rica. After her Fellowship, she has participated in several activities as senior Fellow, sharing her skills with the new generation of Fellows 2016 and also constantly involved in content development for School of Data.

Juan Manuel Casanueva

Juan researches and promotes ICT for Social Change projects in Latin America. He is the CEO and co-founder of SocialTIC, a non-profit that enables changemakers through the strategic use of ICTs. He was ICFJ Knight Fellow 2014-2015 focused on enabling ICT and data-driven journalism in Mexico and Central America.

Sylvia Fredriksson

Sylvia is designer and project coordinator at École de Données (School of Data France). Her work is dedicated to civil society empowerment through design and technology. She now works as a designer-researcher at the Cité du Design in Sainte-Etienne, France. She specialised in Hypermedia at Paris 8 University and regularly teach design classes.