Category Archives: Uncategorized

Field Papers: How To

At the Indiranagar Data Party! Garbage Go! they had a few people who didn’t want to use technology to map garbage so Maanya and Aarthy printed out Field Papers for mapping. These worked really well and allowed for a more inclusive event.

Maanya from Mapbox made a how to for using Field Papers.


Step 1: Click on Make


Step 2: Go to the area you want to map and select with the rectangle.


Step 3: Download and print.

They will look like this and you can give them to people to map along the way.


Data Party! Garbage Go!

At DataMeet we have spent years looking for and trying to make data accessible. The last few years more and more data is being made public which we are excited about however people demand data that fills the gaps in data that already exists or that is more actionable. Data that people want and need isn’t being produced, and if it is being produced it isn’t being shared.

This is the most true in urban spaces where there are tons of projects dedicated to collecting data for the city but none of this data enters the public domain as open data. It isn’t public data because the government doesn’t collect it and the various governance and civic oriented groups who collect the data are more prone to write reports or put the analyzed data up online and not the usable and complete raw data.

So DataMeet along with Oorvani Foundation and Mapunity want to start a monthly Data Party! Where we pick a topic and try to collect as much data as we can over a month. Then we will make the data open for download on OpenCity Urban data portal and also send it to the appropriate person in the government, as well as, write data stories on Citizen Matters.

So please join us on Sept 24th to kick off the first ever Data Party! Garbage Go! 

There are an estimated 9000 garbage blackspots in Bengaluru. We are trying to catch them all!

Sign up to map your neighborhood everyday. Or join us for chai and snacks on Sept 24th and map with friends in 3 locations: Koramangala, Indiranagar or Frazertown.

You have to register and download the app so we can plan for the snacks.

Event location will be sent to you once you register.

Time is 9:30am to 12:30am – Sept 24th Saturday morning.

9:30am – Intro and app explanation
10 to 12 – Mapping
12 to 12:30 – Closing and Next Steps.

All data collected will be made open on the Urban Data Portal for download and use, and this data will be sent to the BBMP and followed up on.

Indirangar – Maanya – Meeting place MapBox India

Koramangala – Nitin – Meeting place Sagar Fast Foods behind BDA complex

Frazertown – Contact Nisha Thompson – Meeting place French Loaf by Richards Park.

Register here.

Download the app and get mapping.

Link to Mapunity Groups IOS app:
Link to Mapunity Groups Android app.
SeeRead more 

Sikkim Data Portal and Sensitive Information

Sikkim was the first state to come up with its own Sikkim Open Data Acquisition and Accessibility Policy (SODAAP) on the lines of National Data Sharing and Accessibility Policy (NDSAP).  Continuing to lead Sikkim is now officially the first state to have its own data portal we are really happy to see this development and hope more states follow.  DataMeet has been carrying consultations with officials of Sikkim in framing the policy and helping them with workshops and insights to use the data. Honorable Member of Parliament Dr. Prem Das Rai has also been our keynote speaker during the Open Data Camp 2015 at Delhi sharing experiences about the on-going work in Sikkim.

As emails were being pushed about the launch of the portal on 15th July, we were alerted about sensitive data being published through the data portal by Abhay Rana. Two datasets on the portal had sensitive information like 1) name, 2) religion, 3) caste, 4) father’s name, 5) mother’s name, 6) gender, 7) birth date, 8) residential address, and 9) information regarding disabilities (if any) of school children, teachers with additional detail of marital status for the teachers.  We alerted both NIC and the chief data officer in charge for the datasets to get them taken down immediately.  Open data does not promote any sensitive information being shared publicly and it violates the very core principles. We applaud the quick response by the data controller in response.

It was an unfortunate accident that sensitive information not to be published under the policy was shared through the data portal. NDSAP along with SODAAP has mandates for every department to make sure sensitive information has restricted access and is not to be published. This incident is not the first where we encountered sensitive information was being published by government officials. Most of the times such information is in the public domain by accident or due to lack of awareness among officials about type and parameters available under the datasets. More incidents like this can harm officials from publishing further data and is a threat to the ecosystem of open data.

As more and more data becomes part of the public domain it is important that we all can work together to ensure that we do not violate privacy or put up sensitive data. More guidelines and frameworks are needed to maintain and report sensitive data which is already public.

We request you to bring to our attention if any sensitive information is being published under the pretext of open data. For now explore the new data portal and use open data to bring positive change in your community.

12 DAYS TIL 2016 Bangalore Open Data Camp: Pollution Party!

DataMeet will be hosting the 5th Bangalore Open Data Camp: Pollution Party on May 14th and 15th.  This year we want to spend time and look at the growing problem of pollution by spending two days examining the role of data. Last year saw a major turning point in the debate around pollution. Indian cities became a major focal point, as proof that New Delhi has worse air quality than reigning champion Beijing was proven with data. This put a spotlight on air pollution problem across India. At the same time water pollution from industry has also come up in the foaming lakes and rap videos fighting for recognition of pollution and its effects on people. The economic and development growth has meant that the building industry has been in over drive bringing sand and dust into urban and peri urban areas in large quantities plus the growing lack of proper trash disposal has had major health implications for people from all social economic backgrounds.

However, the actual exposure of pathogens and pollution is not well known, extensive data has not been made available or is being collected in a way that can’t be easily understood or acted upon. This has spurred the rise of data collection networks and agencies to fill this gap. In every major city citizen supported cheap sensor devices have been put around cities to add data to the small number of official government monitoring stations.

This year at Open Data Camp we want to explore the role of these data collection network in a growing citizen and private sector monitoring role. What is the role of open data? When these networks grow can there be agreement on standards and formats to be maintained? and Are there financially sustainable solutions that can be built on open data?

Notably Karnataka State Pollution Control Board is attending to give the keynote in the morning and hopefully bring some data with them for us.

Tentative Agenda

1) Karnataka Pollution Control Board

2) Environmental Groups to give the general ecosystem around enforcement

3) Data collection networks
Sensors without Borders
Hindustan Times*
YUKTIX – Open Weather Network Bangalore
India Open Data Association

4) Water Pollution
Ground water
Urban lakes

5) What you can do with robust data?
Urban planning
Modeling for enforcement.

6) Open Environmental Formats and Information Discussion

Day 2

We will be hosting a sensor workshop for kids

Sensor workshop poster

We’d like to thank our sponsors Google, Sensor without Boards, India Open Data Association, Oorvani Foundation, and partner Reap Benefit. If you would like to sponsor or get involved please contact me @ Nisha (at)

Meet a DMer: Dilip Damle

On the DataMeet list we have started referring to each other as DMers.  So I wanted to start highlighting people who are pretty interesting and have a great insights into open data.

Dilip has been a major contributor to the list for a few years. He is always sharing data, advise, and information. He has contributed to the pincode and shapefile conversations and it always a source of support.

Where are you from? What do you do? 

I am from India, born and studied in Goa. Presently (last 28 years) based in Delhi.

By qualification I am a Mechanical Engineer.
Presently I am a freelancer (worked as an employee between 1981 and 1992)  As a one man SOHO professional I provide services to different Private organisations for themselves and some  Private organisations in turn providing services to Government agencies. Area of specialisation is mainly application of computers to Engineering, CAD, Technical publications, Cartography, Data Maintenance,  MIS reports and custom software.

I am a part time hobby programmer and have been programming since 1983 for fun and to automate my own work– VB, VBA and Autolisp.

How did you find out about DataMeet? 

I wanted to make and publish editable version of Election maps and was looking for the source of updated maps after delimitation.  I bumped in to [Raphael] Susewind’s Blog and via that page came to know about Datameet.

Why are you interested in data?

Mainly to make editable maps in common software, which I have a plan to offer free. More recently I have been doing less work on CAD and more on databases. In the process I am also hooked to the beauty of clean data represented especially in Database as against Excel.

Do you believe in open data? and why?

Yes, At least the data that is relevant to society as a whole.


  1. Only open data can be that Single Truth. Otherwise multiple mismatching versions float around for commercial reasons.
  2. There are no unnecessary fights over wrong data.
    (The most classic example is the India’s Boundary map. In this world of computers we have not provided a “Correct” Boundary accessible to all in a digital format and and want to stop all “Incorrect” data freely available just by legislation and expecting everyone to hold a print in your hand and come to Dehradun for “approval”. It is ridiculous.)
  3. Let there be commercial exploitation by value addition like visualization, Web Access but raw data generated by agencies that run from taxpayer’s money should be available in the open. Except for security, military and personally identifiable data.

What do you hope to learn?

I hope to interact with varied people and know newer things and techniques  that I might not have even heard of before.

What is your impression of the DataMeet community?

Good people but It is too small, needs to be bigger.

What kind of civic projects do you work on? What kinds of civic projects are you interested in working on?

I have worked on Water Supply and  Sewer networks mainly the application of computers for several years. A little on Storm water.
In future I wold love to work on Transportation modeling.

Share a visualization that you saw recently that made a big impression?  Share an article you have read recently that made a big impression? (does not have to be data related)

Share a visualization that you saw recently that made a big impression? Share an article you have read recently that made a big impression? (does not have to be data related)

A visualisation about Evolution.Evo_large

Open Access Week 2015

Late post

Open A20151024_190330ccess Week is used as an opportunity to spread awareness of open access issues throughout the world. It was Oct 24th to the 30th last year. Shravan and Mahroof from the Ahmedabad Chapter suggested we do the first every multi city hangout and bring together different groups working on openness issues throughout the country.

For the event we had a Google Hangout with:

Data.Gov.In started us off with  Alka Misra and Sitansu participating from Delhi. They spoke about new features on, new datasets and visualizations available. They were also there to extend invites for more participation from the community.

Rahmanuddin from Access to Knowledge then spoke about Wikipedia and their community dedicated to local language knowledge sharing. They also had pertinent questions to Data.Gov.In regarding using open licenses. Since Wikipedia can’t use any data from Data.Gov.In since a license isn’t specified.

Ahmedabad Chapter went next. Ramya Bhatt, Assistant Municipal Commissioner from Ahmedabad, came and gave a brief talk about their plans for open data and smart cities. Alka from Data.Gov.In offered assistance. Then some students from Dhirubhai Ambani Institute of Information and Technology’s machine learning program used some data from to do analysis at the event. They looked at high budget allocation per state and drop out rates.

Open Access India’s Sridhar Gutam briefly went through the plans OAI has for the upcoming year to promote open access science and journals.

Hyderabad DataMeet is a new and yet to really take shape meet up but we were happy to see a first attempt. Sailendra took the lead as the organizer and brought together some people from IIM Hyderabad. Srinivas Kodali was there to talk about all the data he had made available that week.


20151024_184755Banalore DataMeet was there to share what has been going on with DataMeet and any new iniatives in Open Access



It was a great event, and as with all online events there were some technical difficulties but everyone was patient. It was awesome to see how the open culture space has grown, and to see so many new DataMeet chapters.

You can see the event below:

I hope we do one again soon minus the technical difficulties.

Global Open Data Index: Water Quality

Last year I helped assess the water quality section of the Global Open Data Index (GODI). Given the news of lead poisoning in Flint, Michigan and increasingly beyond, safe drinking water is no longer assured even in countries where it’s been guaranteed, so I am very glad they included it in GODI.

GODI is a survey of 122* countries that look at the status of ‘high priority datasets’ and whether they are truly open according to the Open Data Criteria. Water quality was included last year for the first time. So my job was to examine each country’s submission  and assess if the data submitted was what was asked for and met the criteria for being open. This was a daunting task but I figured if I could find water quality data in India of all places it wouldn’t be impossible.

Assessment Criteria/Methodology

GODI looked for very specific parameters:

While there are a lot more parameters that could be asked for, these were a good sample of parameters to assess if there is robust water monitoring in the country.

After the initial submission phase there were a lot questions about why wouldn’t the survey just ask for drinking water quality data or environmental monitoring data?

Choosing parameters instead of programmes is important because monitoring the environment and drinking water quality are connected. Some countries haven’t really established large nationalized water treatment strategies, drinking water comes directly from a natural resource so the environmental monitoring data inadvertently applies to the drinking water scenario.  Which means that if a country really has robust water quality data they must have these 5 parameters because they cover surface and ground water sources and also reflect safe drinking water standards.

The assessment would be rejected if a submitter only found the surface water body monitoring stations (environmental water monitoring) for instance because arsenic and fluoride are only found in groundwater. So the submitter would either ideally find the treated drinking water quality data which will cover all the parameters or the source water quality data for both surface and ground water.

For a full look at the methodology of the entire survey go here.

Some background

There is no one way to create water management systems but there are two major ways by which people get water – directly from the source or piped in from a source or a treatment facility. The origins of the water source is important. If you are getting water from the ground there are different quality issues  than from surface water (lake or river). If water is from a treatment plant there is a possibility that plant is getting water from both surface water, ground water, and in some cases recycled water. Usually water quality is measured at source and after treatment (treatment plants take multiple water quality samples during the treatment process.)

A full water quality assessment means lots of parameters and not all of them are tested the same way; some parameters take several days and require specific conditions, others can be taken easily through filters or litmus papers.  Water quality is a deliberate process of sampling and testing, and it not as easy as sticking a sensor into the water and monitor a continuous feed of data (although the potential for these approaches is quickly growing as technology improves.)

What I looked for

Since water quality was a scientific process I figured if I found any proof of water treatment or quality monitoring, a dataset would not be far off. After going through a few countries I noticed that the different water management approaches and policies affected where you would find the data.

Most countries give drinking water treatment responsibilities to local bodies but sometimes is monitored by central government under public health regulation so aggregated data could lie with the public health ministry or the environmental protection body.  In most cases responsibility for environmental monitoring fell to a central government Environmental Ministry.

So this scenario means that multiple datasets exist – a centralized dataset for surface and groundwater that  usually lies with the environmental ministry that could have all the parameters but sometimes doesn’t, or it doesn’t have real time data (this means data  may be available but from less frequent data collection such as quarterly or half yearly efforts). Or the Public Health Ministry has reports of water quality with all the parameters but these are aggregated, and usually in a report form (not a dataset) and not updated in a timely manner.

The US, for instance, falls under this group and can produce confusing submissions. The US has a robust geological survey of surface and ground water sources. However, the drinking water reports are supposed to go to the Environmental Protection Agency but no one seems to be updating the database with information. In my assessment I reduced the score because both are supposed to be available in the public domain.

There are countries like Belgium where water management and monitoring are completely left to the local body and there is no central role for monitoring at all, which meant there is no dataset.

There are countries where there is a strong central role in water management and a dataset could be made open like in France. Korea stood out, because they have live real time water quality information from their treatment plants that gets updated to a website.

Then there are the ‘unsures’: which are countries that seem to treat water to some degree or have national drinking water monitoring programmes but don’t have data online, reports or any mention of data at all. This is not restricted to the developing world. I was very frustrated with several European countries with newspaper articles riddled with reports of how pristine and delicious their water is that don’t have a single public facing dataset.

Take Aways

United Kingdom and the US, both pioneers of the open data movement had terrible water quality data for water treatment, and no effort has been made to bring the data together or make it available in a real time fashion.  Also it is not clear to citizens who holds local bodies accountable for not updating their reports, making reports public or finding ways to bring this data into the light so it can be usable. It is no wonder that the US is now on the cusp of a public health crisis.

It is frustrating that the open data movement hasn’t quite been able to reconcile decentralization and local responsibility with national level accountability and transparency. Public health is a national level issue even though local and regional contexts are required for management. How do we push for openness and transparency in systems like this?

In places like India where water quality treatment is largely left to private players and huge populations are not receiving treated water, the need for data to be available, open, and in the hands of central bodies but also local players is a must, because people need to try to find solutions and where to intervene. Given the huge problems with water borne diseases, the slow but epic arsenic and fluoride poisonings gripping parts of India, and the effects this will have for generations, making this data public, usable and demystified is no longer an option.

All in all, I have to say this was an enlightening experience, it was cool to be able to learn something about each country. In our continuous push for open data we sometimes get lost in standards, formats, and machine readability, but taking a moment to really prioritize our values in society and have open data reflect that is essential. Public health outcomes and engaging with complex issues like it are an essential part of how to grow the open data movement and make it relevant to millions more.

*(Correction: Previous version said the survey included 148 countries, the actual number is 122.)

Bihar Elections

DataMeet has always been interested in doing projects so last year we decided to run a pilot. In the last few years the demand for data work has increased from non profits and journalists and they usually approach data analytics vendors like Gramener. However, these firms can be expensive or have high paying clientele which means that smaller accounts tend to not get their full attention. This leads to an increase in volunteer events like hackathons which don’t always result in finished usable products or can give non profits the long term engagement they need to solve issues. Vendors are not usually privy to the specific data problems a sector has and don’t want to let their tech people invest the time to learn about the subject and understand the particular data challenges. Though the civic tech space is growing, non profits and media houses can’t yet afford or see the need for internal tech teams to deal with their data workload.

With all this in mind we wanted to see if DataMeet can help fill and enrich this space as well as help build capacity within non profits to manage data projects. We were trying to find out, can we assemble teams through the DataMeet network to manage the entire pipeline of data work from clean up to visualization. These wouldn’t be permanent teams but filled with freelancers or hobbyists.

For this first project DataMeet would project manage and Gramener would provde the data analysts, the non profit managing partner was Arghyam and the ground partner was Megh Pyne Abhiyan. Megh Pyne Abhiyan works in several districts in north Bihar on water and sanitation issues. They wanted to use data to tell the story of what the status of water and sanitation was in those districts as a way of engaging with people during the election. It was decided we would do water and sanitation (WATSAN) status report cards for 5 districts — Khagaria, Pashchim Champaran, Madhubani, Saharasa, and Supaul — using government data.

This was an exciting project for us because it would be the first time DataMeet would work with a partner who works on the ground and the output would be for a rural, non online, non-English speaking audience.

DataMeet would project manage the process of data cleanup, analysis and visualization (which the team from Gramener would do) and then give the report cards to the Megh Pyne Abhiyan for them to do the translation and create the final representation of the report cards for their audience.

The Data

The partner wanted the data to be mapped to Assembly Constituencies, they wanted analysis for following situations

  1. Sanitation coverage for each Assembly Constituency and Gram Panchayat.
  2. Water quality, what is the contamination situation of the district, Assembly Constituency and Gram Panchayat.
  3. Water access, how do people get their drinking water.

It was also important to understand this data in the context of the flood prone areas of Bihar. For instance if there is an area that gets drinking water from shallow wells, with little sanitation in a high flood area those areas can suffer from high levels of water borne diseases.

The data we got was from

Since we were doing report cards based on Assembly Constituencies we needed the data to be at the Gram Panchayat (GP) level. Luckily the MDWS does a good job of collecting data all the way down to habitation so GP level data was available.

There is no official listing of what GPs are in which Assembly Constituency so the partner was asked to split the data by AC so we wouldn’t have to do that mapping. They agreed they knew the area better and would have the resources to pull together all the GP level data into organized Dropbox folders grouped by districts then split into ACs.

Data Cleanup

We received one PDF file per GP,  for water access and number of toilets, water quality was given in one large file by district.

All the data we received was in PDF. This was a huge hurdle as the data was from the government information management system so it was from a digital format but rendered in a PDF this meant that we had to convert unnecessarily. However, since the ground partner picked the data they needed and organized it by AC we wanted to make sure we were using the data they specified as important. So we decided to convert the data. This job was done by Thej and I and was extremely manual and time consuming and caused some delay in the data being sent to the analysts.  (See how we did it here.)


The analysis required was basic. They needed to know at an AC level what the sanitation coverage was, the sources of water, how people were accessing it and what the water quality situation is.  Rankings compared to other districts and ACs were done to give context. Rankings compared to other districts and ACs were done to give context.So in all the analysis stage didn’t take much time.

Example of Analysis



The UNDP along with the Bihar State Disaster Management Authority had created a map of diaster prone areas including flood. It was in PDF so we asked the folks at Mapbox India to help out with creating a shapefile for the flood map so we layer flood areas onto the Assembly Constituencies.

Bihar AC map with flood prone areas


While we had AC maps we didn’t have GP level maps. They didn’t seem to be available and we couldn’t find them in PDF form either.

Since the election is staggered by district we started with Khagaria. After the initial report cards were done the partner wanted just the cleaned up data in tables to use for their meetings. So we then decided to do the report cards, clean up the data and send the spreadsheets over to them.

As we were processing the next 4 districts I found GP level maps of Bihar, with boundaries of ACs included. This was quite exciting and I thought since we had some time we could do maps for the four pending districts.

After receiving the analysis for the next district I decided that since it would take to long to trace the PDF maps, so the analysts could map the GPs, I would just over lay them onto our AC shapefiles in Photoshop. I was going to put icons or circles in the center of the GP and that would be the map. While tedious I figured it would be worth it to show the maps to the ground partner.

However, when I started mapping I realized that analyzed data wasn’t matching up with the GPs on the map. The GPs listed in the Assembly Constituency in our original folders were incorrect, which meant all the analysis was wrong. Everything had to be checked against the maps and reorganized in the final datasets and then reanalyzed. This caused a huge delay.

On top of that the GPs on the map were spelled differently than in the MDWS data, and every dataset potentially had a different spelling of a particular GP. Which meant the remapping of the data had to be done manually looking at the map, the data, other sources, and sometimes guessing if this was the correct GP or not. This ended up being a manual process for every AC, as we didn’t do this mapping and standardization in the beginning.

While the delay caused problems with the maps being used in the election, they were worth doing to understand the problems with the data and the ground partner identified with the maps the most. By the end we were able to produced districts posters for the different parameters.

Sample report card


Final Posters

PC_sanitation copy poster madhubani_wateraccess copy poster madhubani_sourceprofileposters madhubani_sanitation poster Supaul_wateraccess copy poster copy Supaul_sourceprofile poster copy Supaul_sanitation copy poster copy Saharsa_wateraccess copy poster copy Saharsa_sourceprofile poster copy Saharsa_sanitation copy poster copy PC_wateraccess copy poster PC_sourceprofile poster


Lessons for next time

We learned a lot from this process. Mainly that the issues with standardization of Indian names in data is a real concern. While initiatives like Data.Gov.In are an important first step, it will take real will and dedication to work out this problem.

NGOs and groups that don’t work with data at the scale of modern data techniques are not always familiar with issues like formats, standardization problems, data interoperability,visualization and mapping to other datasets. This means that more time needs to be spent getting the intentions of the project out of the partner not just outputs. Problems like PDFs are not things everyone thinks about so the extra time of working with the partner to understand what data they want and find way to get it are better spent then converting PDFs to CSV if we don’t have to.

Designers are important, I created and designed the maps and posters, while I’m proud of them, they could have been done better and faster by a trained designer. Designers are worth the money and effort in order to make the final product really reflect the care and work we put into the data.

I consider this experience a success, despite the setbacks, we learned how to manage a team that was not full time and how important the initial work with the ground partners are to create realistic deliverables and timelines.

You can get all the data on DataMeet’s github page. 

Big thanks to the Gramener team – Santhosh, Pratap and Girish for dedicating their free time to this.



Sikkim State Government passed an open data policy Sikkim Open Data Acquisition and Accessibility Policy in 2014. With pushing from the Chief Minister and Member of Parliament the Honorable Prem Das Rai they turned to open data to take control of the state’s data. The Honorable Mr PD Rai has repeatedly mentioned is the lack of access to government information on demand. It is not uncommon for lawmakers to ask questions only to have to wait a day or more for the answer and lose a moment to use that information for decision making.

An Open Data for Human Development Workshop was organized by the International Centre for Human Development of UNDP India, with the Centre for Internet and Society, AKVO, Mapbox and DataMeet co-facilitating the event in Bangalore last June. The aim was to bring together members of the Sikkim government, IT professionals, and open data enthusiasts.


In April before the workshop Sumandro (CIS) and I went to Sikkim to have a pre consultation with the Sikkim government on how to prepare for the large workshop in Bangalore. We met with the MP and the heads of the Rural Development, Health, and IT departments to discuss their plans to implement their open data policy. Then there was a large meeting with all the departments and the MP. We presented different things you can do when data is opened and offered suggestions for how to implement the policy. 20150416_123613The departments took turns discussing their issues regarding implementation; concerns like server space, technology needs, how to create incentives to accurate and timely data uploading were shared.

We presented things for them to think about in a preparation for the June event and for how to work with the open data community in India.

In June the workshop was held as NIAS. Thej gave a session on data tools that can be used to assemble, clean, analyze, publish and visualize data. Some of the tools that he introduced and used during the workshop are

  • Tabula Its difficult to extract data from PDFs. But Tabula allows you to extract that data into a CSV or Microsoft Excel spreadsheet using a simple, easy-to-use interface. Tabula works on Mac, Windows and Linux.
  • Open Refine – is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; extending it with web services; and linking it to databases like Freebase.
  • DataWrapper allows you to create powerful charts very easily.
  • CartoDB is the Easiest Way to Map and Analyze Your Location Data

“Overall interaction was great. Delegates from Sikkim were very interested in DataMeet community and work we do as community. Some part of the workshop was used to introduce the community aspect of Data.”

You can see the full notes of the event at Centre for Internet and Society’s blog.

We are looking forward to see Sikkim be the first state to implement an open data portal using the Data.Gov.In platform.