Centre for Internet and Society

Monitoring Sustainable Development Goals in India: Availability and Openness of Data (Part II)

sumandro — 2016-04-12T04:14:27Z

The Sustainable Development Goals (SDGs) are an internationally agreed upon set of developmental targets to be achieved by 2030. There are 17 SDGs with 169 targets, and each target is mapped to one or more indicators as a measure of evaluation. In this and the next blog post, Kiran AB is documenting the availability and openness of data sets in India that are relevant for monitoring the targets under the SDGs. This post offers the findings for the last 10 Goals. The first 7 has already been discussed in the earlier post.

The first part of the post can be accessed here.

Goal #08: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

There are fourteen indicators to monitor the goal 8 and the data is available for all the indicators mapped to their respective targets. For most of the indicators, the data availability is not what the indicator demands, but has to be derived from the available dataset.

The data can be accessed freely in the public domain for all the indicators. However, for the subparts in some of the indicators, the data is not accessible freely. There is a cross agency dependency over the data, to arrive at the required indicator.

Data is collected annually for most of the indicators, while the indicators, viz., Indicator 8.3.1.: Share of informal employment in non-agriculture employment by sex; Indicator 8.5.2: Unemployment rate by sex, age-group and persons with disabilities, which are measured by the Census or the planning commission the frequency of data collection becomes decennial or quinquennial. And the Indicator 8.8.2 : Number of ILO conventions ratified by type of convention, which lists the number of conventions the frequency cannot be determined as it's just a list updated whenever there is a ratification of any ILO conventions. Some of the available data are restricted to particular years and most of them are not till date.

Two indicators, i.e., Indicator 8.5.2 and Indicator 8.10.1: Number of commercial bank branches and ATMs per 100,000 adults, which are measured at the level of districts, whereas Indicator 8.7.1: Percentage and number of children aged 5-17 years engaged in child labour, per sex and age group; Indicator 8.8.1: Frequency rates of fatal and non-fatal occupational injuries by sex and migrant status, are measured at the state level. The remaining are measured only at the national level.

Most of the data are collected from the international organisations like ILO, UNEP, UNWTO, etc., from whose source the data are not updated regularly. There is also a need to disaggregate according to the indicator.

Goal #09: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

When development is through industrialization, sustainable and inclusiveness should be the necessary conditions to attain it. Having said this, the data is available for all the indicators, i.e., twelve indicators, corresponding to the targets as defined for the goal 9. For most of the indicators, the data have to be derived for the required measure to monitor the goal.

From among these indicators, the data is collected annually for most of the indicators, while for the two indicators, Indicator 9.3.1: Percentage share of small scale industries in total industry value added; Indicator 9.3.2: Percentage of small scale industries with a loan or line of credit, the frequency of data collection is once in five years.

Excluding two indicators, i.e., Indicator 9.2.2: Manufacturing employment as a percentage of total employment; Indicator 9.1.1: Share of the rural population who live within 2km of an all season road, for which the data is available at the state level and district level respectively, for the remaining indicators the data is available only at the national level.

The data pertaining to eleven indicators are freely accessible in the public domain, however, for the Indicator 9.b.1: Percentage share of medium and high-tech (MHT) industry value added in total value added, the data is not freely accessible. Most of the freely available data are obtained from the international organisations, along with the official data from the government in India.

Goal #10: Reduce inequality within and among countries

Bridging the gap between the global north-south divide through co-operation – social, economical, political, etc., would promote equality. There are twelve indicators for measuring this goal, of which the data is not available for one of the indicators and are available for the remaining indicators.

From the data available, for six of the indicators the data is accessible freely in the public domain, whereas for the five of the indicators – Indicator 10.2.1; Indicator 10.3.1; Indicator 10.4.1; Indicator 10.7.3; Indicator 10.a.1, the data is closed.

Most of the data available are of the national level and for the Indicator 10.7.3: Number of detected and non-detected victims of human trafficking per 100,000, the data includes from the states as well. However, since the goal refers to inequalities within the country as well, the granularity of the data should have been from the state/district level as well.

And, the frequency of data collected are annually for some of the indicators and for some the details cannot be determined or not valid. For most of the indicators the data has to be derived from the available dataset and disaggregated as needed. Also, for some indicators the data is partially available.

Data Not Available:

Indicator 10.7.1: Recruitment cost borne by employee as percentage of yearly income earned in country of destination

Goal #11: Make cities and human settlements inclusive, safe, resilient and sustainable

Housing and the type of settlements determines the human development and the progress of development of a nation. Therefore for monitoring the goal 11 is implicit to human development. There are thirteen indicators to monitor this goal and out of which the data is available for ten indicators and for the three indicators the data is not available.

For three of the indicators the available data is not freely accessible, while for the remaining ones the data is accessible. And for most of the indicators the data has to be derived as needed.

The data is collected annually for most of the indicators and quinquennially for the Indicator 11.5.1, and for some data the data pertains to particular year and there lacks a sequence of data availability.

For four of the indicators – Indicator 11.2.1; Indicator 11.3.1; Indicator 11.6.1; Indicator 11.a.1, the data is available at the state/city level along with national level. And for the remaining indicators the data is available at the national level alone. Also, some of the data are not up-to-date and refers to data more than 3 or years old.

Data Not Available:

Indicator 11.3.2: Percentage of cities with direct participation structure of civil society in urban planning and management, which operate regularly and democratically
Indicator 11.7.1: The average share of the built-up areas of cities that is open space in public use for all, disaggregated by age, sex, and persons with disabilities
Indicator 11.b.1: Percentage of cities implementing risk reduction and resilience strategies aligned with accepted international frameworks (such as the successor to the Hyogo Framework for Action on Disaster Risk Reduction) that include vulnerable and marginalised groups in their design, implementation and monitoring

Goal #12: Ensure sustainable consumption and production patterns

Production and consumption should go hand in hand, but over consumption or over production would only lead to destruction of the environment. Therefore goal 12 seeks to ensure a sustainability in both. The data is available for ten indicators out of twelve indicators, and for the two indicators the data is not available, so as to monitor the respective goals. Some of the data are partially available and using the available data the indicators can be derived.

Moreover, the data for six of the indicators which are available are freely accessible in the public domain whereas for the remaining four indicators – Indicator 12.4.1; Indicator 12.4.2; Indicator 12.5.1; Indicator 12.b.1, the data is not open.

While for most of the indicators say, Indicator 12.2.1; Indicator 12.3.1; Indicator 12.5.1; Indicator 12.a.1; Indicator 12.c.1, the data is collected annually, whereas for the others, the data which are available are for particular years or cannot be determined. Except for the Indicator 12.5.1, for which the data is available at the city level, the data for the remaining are of the national order. The data is collected from both the national institutions, ministries and also from the international organisations.

Data Not Available:

Indicator 12.1.1: Number of countries with SCP National Actions Plans or SCP mainstreamed as a priority or target into national policies.
Indicator 12.8.1: Percentage of educational institutions with formal and informal education curricula on sustainable development and lifestyle topics

Goal #13: Take urgent action to combat climate change and its impacts

The impact of climate change is severe, therefore taking an urgent action ensures could reduce the impact. The data is available for four of the indicators out of five, and for one of indicators the data is not available.

The data for three indicators are freely accessible in the public domain, whereas for the Indicator 13.3.1: Number of countries that have integrated mitigation, adaptation, impact reduction and early warning into primary, secondary and tertiary curricula, the data is not open and also not specific to the indicator. The data for some of the indicators are partially available and have to be derived.

The frequency of the data is not uniform and cannot be determined, by the virtue of the indicator itself. For example, the occurrence of a disaster event is random. However, for some of the indicators the reporting is either annual or quadrennial.

The data availability is at the national level and in case of the Indicator 13.3.1., the data is available for two states – Orissa and Tamil Nadu. Data for almost all the indicators are obtained from international organizations and very less data availability from the national databases.

Data Not Available:

Indicator 13.2.1.: Number of countries that have formally communicated the establishment of integrated low-carbon, climate-resilient, disaster risk reduction development strategies

Goal #14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development

Oceans are the torchbearers for all the countries. Therefore everything related to oceans, seas and marine resources have an impact on the human life. There are ten indicators corresponding to the targets, of which the data is available for nine indicators and for one indicator the data is not available. The data for some of the indicators are not direct, but need to be derived, while for some indicators the data is partially available. To derive some indicators we need to rely on cross agency data.

For the Indicator 14.a.1: Budget allocation to research in the field of marine technology as a percentage of total budget to research, the data on budgetary allocation doesn't specify to marine technology.

The frequency of data collected for most of the indicators are not available or cannot be determined or not applicable, whereas for some the data is collected annually. And for most of the indicators the data is available at the national level and for the Indicator 14.5.1: Coverage of protected areas in relation to marine areas, the data is available for the states also.

Data Not Available:

Indicator 14.6.1: Dollar value of negative fishery subsidies against 2015 baseline

Goal #15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss

This goal on restoring, promoting ecosystem and stopping biodiversity loss, etc., has fifteen indicators mapped to twelve corresponding targets. Of which, the data is available for fourteen of the indicators and the data is not available for the one of the indicators. Data for some of the indicators exist partially and for some the data has to be derived to match the indicators. To arrive at the indicators, the data has to be derived from different datasets available.

Most of the data which are available are closed and only five are accessible in the public platform – Indicator 15.1.1 : Forest area as a percentage of total land area; Indicator 15.4.2: Mountain Green Cover Index; Indicator 15.8.1: Adoption of national legislation relevant to the prevention or control of invasive alien species; Indicator 15.9.1: Number of national development plans and processes integrating biodiversity and ecosystem services values; Indicator 15.a.1: Official development assistance and public expenditure on conservation and sustainable use of biodiversity and ecosystems.

The frequency of data collected is not available or cannot be determined for majority of the indicators, while the data is annually collected for the ones which can be determined. Furthermore, the data is available at the national level for all the indicators, except the Indicator 15.b.1: Forestry official development assistance and forestry FDI, for which the data is available at the level of states as well.

The data available are collected by international organisations like OECD, FAO, Convention on Biological Diversity, etc., as well as by the national institutions and ministries like Planning Commission, Ministry of Environment, Forest and Climate Change, etc.

Data Not Available:

Indicator 15.2.2: Net permanent forest loss

Goal #16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels

A society which is inclusive, peaceful, provides justice and accountable in all its forms would ensure sustainable development, therefore to promote the aforementioned parameters one has to monitor them through an established measure. There are twenty-one indicators for this goal mapped to the respective targets and out of which the data is not available for five indicators to monitor the goal. From the available dataset, the values need to be derived for some of the indicators and for some indicators the data is directly/partially available.

From among the data which are available, for nine indicators the data is not freely accessible in the public platform, while the remaining six data set are open to access. They are available both from national and international agencies and most of the data are not up to the date.

The data which are available are collected/reported annually. And, excluding four indicators. i.e.; Indicator 16.1.3, Indicator 16.3.1, Indicator 16.4.2, Indicator 16.b.1, the data is available at the state level, while for the remaining indicators the data is available only at the national level. Most of the indicators require data from past 12 months, but the available dataset does not cater the needs, as they are not updated regularly. Finally, the indicators seeks disaggregated data for monitoring the goal.

Data Not Available:

Indicator 16.1.4: Proportion of people that feel safe walking alone around the area they live
Indicator 16.2.3. Percentage of young women and men aged 18-24 years who experienced sexual violence by age 18
Indicator 16.6.2: Percentage of population satisfied with their last experience of public services
Indicator 16.7.2: Proportion of countries that address young people's multisectoral needs with their national development plans and poverty reduction strategies
Indicator 16.a.1: Percentage of victims who report physical and/or sexual crime to law enforcement agencies during past 12 months disaggregated by age, sex, region and population group

Goal #17: Strengthen the means of implementation and revitalize the global partnership for sustainable development

Moving towards achieving SDGs in the global scenario requires support – financial, technological, etc. This support can be strengthened the relationship between the developing and the developed countries. There are twenty-four indicators to monitor the goal 17, out of which the data is available for twenty-three of the indicators and for one of the indicators the data does not exist.

The data which are available are direct as per the indicators, whereas for most of the indicators the data need to be derived. Data is partially available for the Indicator 17.16.1: Indicator 7 from Global Partnership Monitoring Exercise: Mutual accountability among development co-operation actors is strengthened through inclusive reviews.

From the data available for twenty-three indicators, fourteen of the data set are freely accessible and the nine are not open. Also, some of the data which are open are not up to date or the latest data is not open.

The data is collected annually for most of the indicators and for some the data is available for particular year. Also for some of the indicators like Indicator 17.5.1: Number of national & investment policy reforms adopted that incorporate sustainable development objectives or safeguards x country; Indicator 17.6.1: Access to patent information and use of the international intellectual property (IP) system; Indicator 17.18.2: Number of countries that have national statistical legislation that complies with the Fundamental Principles of Official statistics, the frequency cannot be determined or not valid.

Since this indicator speaks at the national level, the granularity of the data pertains to the nation. Most of the data are obtained from the international organisations say UN, World Bank, IMF, OECD, etc., and some are from the national institutions/ministries like Planning Commission, Finance Ministry, etc.

Data Not Available:

Indicator 17.17.1: Amount of US$ committed to public-private partnerships and civil society partnerships

Conclusion

Decision making depends on data, a data should be representative, with high quality and has to be timely collected, which ensures precise assessment of the decision being made. From the analysis it was found that, most of the data which are available are either not freely accessible, outdated and not precise to the need. Most of the SDG indicators are based on disaggregation. The disaggregation is a key to measure to the precision, especially incidences like poverty, food security, health, etc. Therefore, to monitor different parameters we need to identify the different levels prevailing in the parameter to ensure inclusivity.

Said above, the frequency of data collection is either annual, quinquennial and decennial. To enable real time evaluation, the data should be up-to-date. Moreover, for most of the indicators the data availability is at the national level or at the state level and sometimes at the district level. The granularity of data ensures geographic inclusiveness.

In a country like India for close monitoring of progress/development of any sort the data availability should be;

at a granular level of district/block,
collected and updated regularly,
disaggregated by age, sex, and also by social group, and
the data should be open to be able to access in the public domain freely.

Open data will be a crucial tool for governments to meet the transparency and efficiency challenges. For this reason, government data should be open – freely accessible, presented in a format that is comparable and reusable and, ideally, released in a timely manner.

Author

Kiran A B, is a student of Master of Public Policy (MPP) at the National Law School of India University, Bengaluru. Kiran has an undergraduate degree in electronics and communications engineering, and he has three years full-time work experience as a software engineer, working in different technological platforms. His research interest includes interdisciplinary linkages between policy, law and technology.

For more details visit https://cis-india.org/openness/monitoring-sustainable-development-goals-in-india-availability-and-openness-02

Meeting on Proactive Disclosure and Personal Data (Delhi, May 13, 5:30 pm)

sumandro — 2017-05-13T04:32:41Z

CIS is organising an informal discussion on topics related to proactive disclosure and personal data thrown up by the recently published report by Amber Sinha and Srinivas Kodali titled "Information Security Practices of Aadhaar (or lack thereof)". Please join us at 5:30 pm today, May 13, at the CIS office.

Read the report: PDF

Location

For more details visit https://cis-india.org/internet-governance/events/meeting-on-proactive-disclosure-and-personal-data-delhi-may-13

MediaNama - #NAMAprivacy: The Future of User Data (Delhi, Sep 6)

sumandro — 2017-09-05T10:22:12Z

MediaNama is hosting a full day conference on "the future of user data in India", on the 6th of September 2017, which is particularly significant given the recent Supreme Court ruling on the fundamental right to privacy, and two government consultations: one at the TRAI, and another at MEITY. This discussion is supported by Facebook, Google, and Microsoft. Sumandro Chattapadhyay, Research Director, will participate as a speaker in the session titled "regulating storage, sharing and transfer of data."

Details

Time: September 6th 2017, 9 am to 4:30 pm

Venue: Gulmohar Hall, India Habitat Centre, Lodhi Road (please enter from Gate #3)

Agenda: https://www.medianama.com/2017/08/223-agenda-namaprivacy-future-of-user-data/

Announced Speakers

Chinmayi Arun, Centre for Communication Governance at NLU Delhi
Malavika Raghavan, IFMR Finance Foundation
Renuka Sane, NIPFP
Smitha Krishna Prasad, Centre for Communication Governance at NLU Delhi
Ananth Padmanabhan, Carnegie India
Avinash Ramachandra, Amazon
Hitesh Oberoi, Naukri
Jochai Ben-Avie, Mozilla
Mrinal Sinha, Mobikwik
Murari Sreedharan, Bankbazaar
Sumandro Chattapadhyay, Centre for Internet and Society

Facilitators

Saikat Datta, Asia Times Online
Shashidar KJ, MediaNama
Nikhil Pahwa, MediaNama

Attendees

We have confirmed 140+ attendees from: Adobe, Amber Health, Amazon, APCO Worldwide, Bank Bazaar, Bloomberg-Quint, Blume Ventures, Broadband India Forum, Business Standard, BuzzFeed News, CCOAI, CEIP, Change Alliance, Chase India, CIS, CNN News18, DEF, Deloitte, DNA, DSCI, E2E Networks, British High Commission, Eurus Network Services, FICCI, Firefly Networks, Flipkart, Forrester Research, Fortumo, DoT, MEITY, IAMAI, IBM, ICRIER, IFMR Finance Foundation, IIMC, Indian Law Institute, Indic Project, Info Edge, ISPAI, IT for Change, ITU-APT, Jamia Millia Islamia, Jindal Global Law School, Mimir Technologies, Mozilla, Newslaundry, NIPFP, Nishith Desai Associates, NIXI, NLU-Delhi, ORF, Paytm, PLR Chambers, PRS Legislative Research, Publicis Groupe, Quartz India, Reliance Jio, Reuters, Saikrishna & Associates, Scroll.in, SFLC.in, Spectranet, The Economics Times, The Indian Express, The Times of India, The Wire, Times Internet, Twitter, and more.

For more details visit https://cis-india.org/internet-governance/news/medianama-namaprivacy-the-future-of-user-data-delhi-sep-6

Mathematisation of the Urban and not Urbanisation of Mathematics: Smart Cities and the Primitive Accumulation of Data - Accepted Abstract

sumandro — 2015-11-13T05:47:13Z

"Many accounts of smart cities recognise the historical coincidence of cybernetic control and neoliberal capital. Even where it is machines which process the vast amounts of data produced by the city so much so that the ruling and managerial classes disappear from view, it is usually the logic of capital that steers the flows of data, people and things. Yet what other futures of the city may be possible within the smart city, what collective intelligence may it bring forth?" The Fibreculture Journal has accepted an abstract of mine for its upcoming issue on 'Computing the City.'

Speaking to Geert Lovink, Wolfgang Ernst explains that '[t]he coupling of machine and mathematics that enables computers occurs as a mathematization of machine, not as machinization of mathematics' [1]. In this paper, I propose that the idea of smart cities be understood not as 'urbanisation of mathematics' – as often described by industry documents, design fictions, and academic analyses – but as 'mathematisation of the urban.' By the notion of 'urbanisation of mathematics,' I indicate at those reports that conceptualise smart cities as data analytics, or civic mathematics, at an urban scale. I explain how this notion is shared by design visions of actors from the networking industry, such as IBM and Cisco, emerging academic practices in urban science and informatics, and calls for urbanising the technologies of regulation and governance, in the sense of making these technologies directly and bi-directionally interact with the urban citizens [2]. Conversely, the 'mathematisation of the urban' perspective foregrounds a specific transformation at hand in the production of urban space itself, which I argue is what is captured in the idea of smart cities. This transformation is not a new thing, and has been heralded by the coming of coded infrastructures and the transduction of urban space through them [3]. The process of 'mathematisation of the urban' refers to a fundamental reorganisation of the urban itself so as to make aspects of it available to mathematical manipulation, most often undertaken by software systems. This mathematisation takes place through the rebuilding of urban infrastructures so as to facilitate sensing and recording of parts of urban lives and processes as mathematical data, and the embedding of coded assemblages that can communicate and act upon the analysis of such data, and also through re-building the relations of property around this newly-obtained and continuously-generated resource of data about the urban.

I propose in this paper that production, circulation, and ownership of data must be considered as a central problematique in the discussions of smart cities. As writings on smart cities have often focused on the dyadic relationships between code and space on one hand, and co-evolution (and splintering) of networked infrastructures and the urban form, the figure of data has remained implicit yet subdued as as an entry point to study the idea of smart cities. Even for commentators who do focus on the implications of data, the category is often treated as a feature or a capacity of new technological assemblages. Instead, I argue in this paper that it is the concerns of production, circulation, and ownership of data that drive the conceptualisation and actual material forms of the visions of smart cities. These technological assemblages, materialisation of which constitute such visions, are implementations of exclusive data collection operations targeting various portions of urban lives and processes. The imagination of 'city 2.0' takes a particularly insightful colour when thought of as an analogy to the 'web 2.0' model of capture and monetisation of user behaviour data. Further, I employ the Marxian theory of 'primitive accumulation' to describe how the material infrastructures of networked sensors and embedded data capture systems create enclosed spaces for conversion of collectively-held-information into data-as-exchangable-and-interoperable-value, through which disparate and distributed knowledge and experiences of the urban is transformed into urban data, which can be centralised and queried, and hence value can be extracted from it.

Footnotes

[1] Lovink, Geert. 2013. Interview with German Media Archeologist Wolfgang Ernst. Nettime-l. February 26. Accessed on April 20, 2015, from http://www.nettime.org/Lists-Archives/nettime-l-0302/msg00132.html.

[2] Sassen, Saskia. 2012. Urbanising Technology. LSE Cities. December. Accessed on April 20, 2015, from http://lsecities.net/media/objects/articles/urbanising-technology/en-gb/.

[3] Dodge, Martin, and Rob Kitchin. 2005. Code and the Transduction of Space. Annals of the Association of American Geographers. 95: 01. Pp. 162-180.

For more details visit https://cis-india.org/raw/smart-cities-and-the-primitive-accumulation-of-data-abstract

Marathi Wikipedia Projects

sumandro — 2015-12-15T08:46:23Z

For more details visit https://cis-india.org/a2k/marathi-wikipedia

Mapping Digital Labour in India

sumandro — 2019-07-20T15:22:01Z

For more details visit https://cis-india.org/raw/mapping-digital-labour

Manuel Beltrán - IoHO - April 01, 2019

sumandro — 2019-04-01T07:57:29Z

For more details visit https://cis-india.org/CIS_RAW_20190404_ManuelBeltran.png

Manuel Beltrán - IoHO

sumandro — 2019-04-01T06:06:42Z

For more details visit https://cis-india.org/ManuelBeltran_IoHO.jpg

Manuel Beltrán - Institute of Human Obsolescence - Cartographies of Dispossession

sumandro — 2019-04-01T08:00:05Z

Join us at the Delhi office of CIS on Thursday, April 4, at 5 pm for a talk by Manuel Beltrán, founder of the Institute of Human Obsolescence (IoHO), which explores the future of labour and the changing relationship between humans and machine. Cartographies of Dispossession (CoD), their current project at IoHO, explores the forms of systematic data dispossession that different humans are subject to, and investigates how data becomes both the means of production as much as the means of governance.

Image credit: Manuel Beltrán

Institute of Human Obsolescence - Cartographies of Dispossession

The Institute of Human Obsolescence (IoHO) explores the future of labour and the changing relationship between humans and machine. Our work develops from a scenario in which forms of manual and intellectual labour traditionally performed by humans are increasingly automated by new technologies. In this context we investigate and challenge the socio-political and economic implications of new forms of labour, such as the production of data. The IoHO developed several projects exploring the production of data as a form of labour, as a different paradigm through which to interrogate and challenge dynamics of ownership over the production of data and the economic and governance objects emerging through it. Previous lines of inquiry around the framework of Data Labour Rights include Data Basic Income, Data Cooperative, Data Production Labour series, Investigative Discussion Sessions and Data Workers Union.

In this talk founder of the IoHO Manuel Beltrán, will introduce the work of the IoHO and discuss their current project Cartographies of Dispossession (CoD). CoD explores the forms of systematic data dispossession that different humans are subject to, and investigates how data becomes both the means of production as much as the means of governance. The project looks at the implications of how the dispossession of data unequally occurs in different contexts, through different means and for different purposes.

Instruments such as the Right Of Access provided by GDPR emerge from a European context but the flows of data operate in a transnational scale. We are exploring the potential and limits of this instrument in combination with others such as the Right To Information in India as tools to investigate and repossess our production of data across borders. We are particularly interested in feedback and discussing in how to think further about this last part.

Manuel Beltrán

Manuel is an artist and activist. He researches and lectures on contemporary art, activism, contemporary social movements, post-digital culture and new media. As an activist, he was involved in the Indignados movement in Spain, the Gezi Park protests in Turkey and several forms of independent activism and cyber-activism in Europe and beyond. In 2012 he co-founded the art collective Plastic Crowds and since 2013 he is head and co-founder of the nomadic school and artistic organization Alternative Learning Tank. In 2015 he founded the Institute of Human Obsolescence, through which he explores the future of labour, the social and political implications regarding our relationship with technology and the economic and governance systems surrounding the production of data.

http://speculative.capital

https://dataworkers.org

For more details visit https://cis-india.org/raw/manuel-beltran-ioho-cartographies-of-dispossession

Mahima Taneja and Nayantara Mohan Nanda - Data Collection in the Development Sector: Field Insights, Challenges, and Innovations (Delhi, October 06, 5 pm)

sumandro — 2017-10-05T06:01:04Z

It is our priviledge to announce that Mahima Taneja and Nayantara Mohan Nanda of Outline India are the speakers at the #FirstFridayAtCIS talk this month. Mahima will share her experience and anecdotes from the field, highlighting challenges and key concerns related to data collection in the Indian landscape, and the need for integrating qualitative components in evaluation designs. Nayantara will talk about Outline India's upcoming evaluation platform for creating transparency and enhancing accountability of key actors in the development sector. If you are joining us, please RSVP at the soonest as we have only limited space in our office.

Mahima Taneja

Senior Research Associate, Outline India

Mahima Taneja is an experienced researcher in mixed-method research design and evaluation projects. She has worked in the capacity of the key researcher for multiple projects and evaluation studies at Outline India in sectors including gender, WASH, Adolescent SRH, education, infrastructure mapping and urban planning. She has undertaken and managed extensive fieldwork for projects based out of Rajasthan, Jharkhand, Bihar, Haryana, Uttar Pradesh, Karnataka and Delhi.

Some of her projects include evaluation of WASH infrastructure across government schools; exploring hand hygiene practices in rural India; formative evaluation of awareness and practices around SRH among Mahadalit adolescents and piloting use of UAVs for infrastructure mapping and policy making.

She has completed her MA and M.Phil in Political Studies from Jawaharlal Nehru University, New Delhi.

Nayantara Mohan Nanda

Research Analyst, Outline India

Nayantara Mohan Nanda is a Researcher at Outline India. She works on building innovative solutions for data collection and is currently supporting the development of the monitoring and evaluation platform for Outline India.

Her experience in research is varied, having worked in areas of displacement, resettlement and rehabilitation, agri-horticulture, rural livelihoods, marginal ecologies, agriculture and education. She has done extensive fieldwork in Uttarakhand and Madhya Pradesh.

She recently completed her M.A. in Development Studies from Ambedkar University, Delhi.

RSVP

Location

For more details visit https://cis-india.org/openness/events/firstfridayatcis-mahima-taneja-and-nayantara-mohan-nanda-data-collection-in-the-development-sector-delhi-oct-06

Madan Oberoi

sumandro — 2017-04-04T11:59:27Z

For more details visit https://cis-india.org/internet-governance/files/copy_of_madanoberoi.jpg

Madan Oberoi

sumandro — 2017-04-04T11:59:00Z

For more details visit https://cis-india.org/internet-governance/files/madanoberoi.jpg

Life of a Tuple: National Register of Citizens (NRC) and the Reform of Citizen Identification Infrastructure in Assam

sumandro — 2023-04-27T16:54:24Z

We are proud to announce that a research grant from the Azim Premji University has enabled us to initiate a study of the updation process of the National Register of Citizens (NRC) in Assam, and the resultant reform of citizen identification infrastructure in India. The study is being led by Khetrimayum Monish and Ranjit Singh, along with Sumandro Chattapadhyay.

The research focuses on two specific aspects of the NRC update:

Challenges of legal citizenship: In this context, we will investigate the constitutional acts and provisions for making citizenship claims in India, the historical narratives of identity politics in Assam and its culmination in the exercise of updating the NRC.

Challenges of procedurally implementing the NRC update: Here, we plan to explore the subsequent design process of updating the register.

Starting with the first aspect of legally defining Indian citizenship, the project will document and discuss the various legal processes of defining the bureaucratic process of updating NRC that emerge along two sets of concerns at different levels of Indian government. First, at the state level, we will explore the socio-political tensions around illegal immigration from Bangladesh and the history of identity-based politics in Assam. Second, at the level of the central government, we plan to investigate the constitutional and legal rules and provisions that are used to define citizenship in India.

For more details visit https://cis-india.org/raw/life-of-a-tuple-nrc-assam-citizen-identification-infrastructure

Life of a Tuple - NRC in Assam

sumandro — 2018-01-22T10:50:54Z

For more details visit https://cis-india.org/raw/life-of-a-tuple

Learning

sumandro — 2015-10-05T14:55:54Z

For more details visit https://cis-india.org/raw/learning