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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 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:

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, Research

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: One on One with Daniela Lepiz

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

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 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 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, Research

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:

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, Research

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.

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 described methods to data novices to think more creatively by drawing and using a gallery of their artwork to build confidence to think more critically. He says that this experience is what led to the creation of databasic.io, a website designed specifically to engage learners.

Lucy tells of School of Data’s initial struggles with setting up a one-size fits all online curriculum. They learned through focus groups and testing that a tool-based approach was not helpful or achievable. Instead they needed a people based approach. They then turned to developing a fellowship programme which is very much at the core of the School of Data network.

Both of our guests had strong opinions about building data literacy culture in organisations. A common mistake is made by letting the IT Department provide data training.  Organisations often produce unhelpful data metrics and dashboards that don’t actually help staff get a full picture of progress.

Gaps in the Data Literacy EcoSystem:

  • Toolbuilders not understanding and not building for learners.
  • NGO’s not testing out data driven messages with their audiences before they release them.

Adjusting to a ‘post-fact’ world means:

  • We need to make sure that people understand that data is not necessarily truth, that it is often used as rhetoric and that it carries bias. Data sets should have a biography attached.
  • Narrative wins, so the data presentation methods where the audience is bombarded with facts and figures just doesn’t work. We have to spend more time pulling out the compelling narrative from the data.

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.

Building an Open Data Ecosystem in Tanzania with trainings and stakeholder engagement

Joachim Mangilima - August 14, 2016 in Community, Event report

Open data is often defined as a product: events, portals, hackathons, and so on. But what does the process of opening data look like? In Tanzania, among many other things, it’s a gradual, iterative process of building capacity in Tanzanian government, civil society and infomediaries to manage, publish and use open data. Of late, the open data scene in Tanzania has been growing from strength to strength.

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Participants in an open data training session related to the Tanzanian health sector

The following milestones are testimony to this growth:

  • last September, Tanzania hosted the first ever Africa Open Data Conference (AODC).

  • the drafting of the country’s open data policy ,which is in the final stages of government approval before it can be passed as policy.

  • formation of the Code for Tanzania chapter,which, among others, will spearhead establishment of local chapters of the global Hacks/Hackers community, as well as a flagship civic technology ‘CitizenLab’, with a core team of software engineers, data analysts and digital journalists, who will work with local newsrooms and social justice NGOs.

  • the establishment of Tanzania Data Lab (Dlab), serving as an anchor for the Data Collaboratives for Local Impact (DCLI) programme, which aims at enabling data analysis and advocating for its prominent use in Tanzanian governmental decision-making. Since the exciting news broke that Tanzania will be joining the Global Data Partnership, the DLab has also started working with the Tanzania National Bureau of Statistics, and other stakeholders, to support the process of assessing what data is needed to drive progress, as defined in the Global Data Partnership Roadmap and, ultimately, leverage the data revolution to achieve the Sustainable Development Goals.

The Tanzania Open Data Initiative

June and April saw another round of training organised under the Tanzania Open Data Initiative (TODI) umbrella, geared towards Tanzanian government agencies covering three key sectors: Education, Health and Water. These are collaborative sessions, tailored towards civil servants working with data related to these sectors, which have been running for three straight years since 2014. They focus on building skills about data-management, cleaning, visualizing and publishing data, open data principles for navigating the legal and professional challenges of managing open data innovation and communicating results to a wider audience.

Often, these sessions produce as many questions as answers – “How precisely do we define ‘access to water’ in rural areas?” or “What does an ‘average passing rate’ really mean?” – but this is encouraged. Indeed, we’re already noticing that a primary beneficiary of open data initiatives is the government itself. Although conventionally billed as a tool for citizens, open data can also be a powerful mechanism to reduce frictions among the multitude of ministries, departments, and agencies (MDAs) of a government.

One notable difference between these rounds in April and June, and previous ones, was that there were a few selected participants from civil society in attendance. This enriched the quality of discussion which resulted in increased engagement of all participants during the sessions: their presence facilitated sharing of experiences for mutual understanding, thereby collaboration between the government and civil society.

Open Data in a day

June’s week-long sessions culminated in an “open data in a day” event at Buni Hub, which for the very first time had a strong focus on media and technology developers. It was amazing seeing the enthusiasm and the level of interaction of this group and how excited they were to put into action key takeaways from the session.

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Participants from the media and technology industry at the Open Data in a Day event at Buni Hub.

These activities are testimony of the progress that Tanzania is making in the open data arena and, with similar activities planned for the future, there is good reason to expect the country’s open data ecosystem to experience further growth in strength and quality.


Infobox
Event name: Tanzania Open Data Initiative
Event type: Workshop
Event theme: Open data in practice
Description: Training organized under Tanzania Open Data initiative collaboratively between National Bureau of Statistics and E-Government Agency supported by the World Bank tailored towards civil servants working with data
Trainers: Dave Tarrant ,Emil Kimaryo, Joachim Mangilima, John Paul Barreto
Partners: Open Data Institute (ODI)
Location: Dar es Salaam, Tanzania
Date: 7th – 14th June 2016
Audience: Statisticians, Economists and data managers from ministries and government agencies for the first two sessions and journalists, start ups developers and civil society for the third session
Number of attendees 95 across the three sessions
Gender split: almost 50/50
Duration: 6 days

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