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Care Principles For Indigenous Data Governance

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The Care Principles In Action

Operationalizing the CARE Principles for Indigenous Data Governance in RDM (September 24, 2020)

The usefulness of high-level Indigenous data governance principles relies on both Indigenous communities and research/data communities to understand operative concepts and to apply them preemptively across data ecosystems and lifecycles. Creating opportunities for non-Indigenous understanding and for Indigenous leadership in data processes will contribute to the development of data and data systems that can lead to Indigenous innovation and development.

Care In The Context Of Scientific Data

The FAIR Principles are aligned to the global shift towards open science and open data, promoting data centric criteria that facilitate increased data sharing among entities while ignoring relationships, power differentials, and the historical conditions associated with the collection of data,. These factors continue to affect how ethical and socially responsible data use can occur within the sciences particularly as machine learning approaches accelerate data re-use.

Concerns about secondary use of data, problems with bias and social inequity, and limited opportunities for benefit-sharing, have focused attention on the tension that Indigenous communities feel between protecting their interests in scientific data generated from their lands, waters, and people, while supporting, or being subject to open data and data sharing initiatives,. Indigenous communities face a number of challenges in facilitating the culturally appropriate reuse of scientific data.

Where To From Here

The articulation of the CARE Principles offers an opportunity to find synergies between the FAIR and CARE Principles with actions and responsibilities across the data lifecycle and ecosystem. Here we detail a preliminary set of recommendations for the data community to operationalise FAIR with CARE.

First, make Indigenous data FAIR. The FAIR criteria should apply to already existing and newly created Indigenous data in both Indigenous and non-Indigenous/hybrid datasets that mix Indigenous and non-Indigenous data. However, the use of Indigenous data in hybrid datasets requires a machine-readable provenance for Indigenous data and to signal the decision-making point that needs to be approached to allow or refuse consent to use the data. For Indigenous collections, Indigenous researchers and communities could try out the FAIR Data Maturity Model criteria, placing them at the forefront of the latest work on FAIR and assisting in identifying both actions to take to make existing data FAIR and to create policies and practices to make future data FAIR. In hybrid datasets there is a need to engage with Indigenous researchers and communities to co-produce policies and practices that can rectify the unFAIRness of existing data and ensure the FAIRness of newly created or deposited data alignes with the CARE Principles.

Fig. 2

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Ocap Ownership Control Access Possession

The First Nations principles of OCAP® indicate how First Nations’ data will be collected, protected, used, and shared. It is a set of principles designed to protect First Nations’ ownership and jurisdiction over their information and data.

Ownership: The relationship of First Nations to their cultural knowledge, data, and information. A community or group owns information collectively in the same way that an individual owns his or her personal information.

Control: First Nations, their communities, and representative bodies are within their rights in seeking control over all aspects of the research and information management processes that impact them. Can include all stages of a particular research project from start to finish. The principle extends to the control of resources and review processes, the planning process, management of the information and so on.

Access: First Nations must have access to information and data about themselves and their communities regardless of where it is held. This also refers to the right of First Nations’ communities and organizations tomanage and make decisions regarding access to their collective information.

Possession: The physical control of data. Possession is the mechanism by which ownership can be asserted and protected.

Fair And Care Data Principles

Operationalizing the CARE Principles for Indigenous Data Governance ...

Technology has helped facilitate the growth of data sharing and the rise of open data a movement that DataStream is proud to be part of. When water data is open and accessible it can be used to better inform decision-making and stewardship efforts.

But how data is managed has a big impact on how useful it can ultimately be. In this post we take a look at two important and complementary sets of guiding principles that underpin best practices when it comes to data stewardship and access.

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The Emergence Of Indigenous Data Sovereignty

Since the 1970s, there has been a resurgence in discourse around Indigenous knowledges, identities, and rights, culminating in the 2007 United Nations Declaration on the Rights of Indigenous Peoples . UNDRIP reaffirms Indigenous Peoples rights to self-determination as political entities and honors the principle of Indigenous control over Indigenous data . The rights articulated in UNDRIP also reflect discourse around Indigenous Cultural and Intellectual Property Rights and Indigenous research ethics . UNDRIP reflects a broad approach to Indigenous data that is not restricted by mainstream conceptions of knowledge and intellectual property .

Indigenous Peoples data include data generated by Indigenous Peoples, as well as by governments and other institutions, on and about Indigenous Peoples and territories. As well as information about Indigenous communities and the individuals, Indigenous and non-Indigenous, that live within . Indigenous Peoples data comprise information and knowledge about the environment, lands, skies, resources, and non-humans with which they have relations information about Indigenous persons such as administrative, census, health, social, commercial, and corporate and, information and knowledge about Indigenous Peoples as collectives, including traditional and cultural information, oral histories, ancestral and clan knowledge, cultural sites, and stories, belongings.

Creating Awareness Of The Care Principles In Action

GIDA became an organisation member of the RDA in late 2019, to support collaborations that promote Indigenous leadership in data processes and create opportunities for general understanding and application of Indigenous rights and interests across data ecosystems . The two examples of the RDA-GIDA partnership below move toward the development of data and data systems that leverage Indigenous rights, innovation and development.

Operationalising FAIR & CARE

After the release of the CARE principles, the RDA International Indigenous Data Sovereignty Interest Group and the RDA FAIR Data Maturity Model Working Group began brainstorming what operationalising the FAIR principles with the CARE principles might entail. A joint meeting at the RDA Plenary 15 explored CARE in the context of scientific data, FAIR in the context of Indigenous data, the intersection of FAIR and CARE, and what would be needed to operationalise FAIR with CARE. A manuscript and subsequent workshop are forthcoming, which will start developing the criteria for the implementation of the CARE Principles. One of the key challenges, in contrast with FAIR, is that CARE informs use and needs to be applied across all stages of a data lifecycle.

GIDA-RDA COVID-19 Guidelines for Data Sharing Respecting Indigenous Data Sovereignty

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Using Indigenous Standards To Implement The Care Principles: Setting Expectations Through Tribal Research Codes

  • 1Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States
  • 2Native Nations Institute, Udall Center for Studies in Public Policy, University of Arizona, Tucson, AZ, United States
  • 3Library and Information Sciences, School of Information, University of Arizona, Tucson, AZ, United States
  • 4Law Library, School of Law, University of South Carolina, Columbia, SC, United States
  • 5Department of Sociology, College of Social Sciences, University of California, Los Angeles, Los Angeles, CA, United States
  • 6American Indian Studies Program, College of Social Sciences, University of California, Los Angeles, Los Angeles, CA, United States
  • 7Center for Human Development, College of Health, University of Alaska Anchorage, Anchorage, AK, United States
  • 8Te Kotahi Research Institute, University of Waikato, Hamilton, New Zealand
  • 9Institute for Society and Genetics, University of California, Los Angeles, Los Angeles, CA, United States
  • 10Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
  • 11Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Operationalizing Care And Fair

Operationalising the CARE Principles for Indigenous Data Governance

Today there is a paradox of scarcity and abundance for Indigenous data,. There is a scarcity of data that align with Indigenous rights and interests and which Indigenous Peoples can control and access in a manner consistent with the CARE Principles. There is an abundance of data that are buried in larger collections, hard to find, mislabelled, and controlled by others in a manner inconsistent with the FAIR and CARE Principles. These two trajectories are represented in Fig. which illustrates that data could be subject to both CARE and FAIR and create different outcomes depending on how implementation criteria have been operationalized within cyber infrastructures.

Fig. 1

Tribal databases and Indigenous Content Management Systems hold tribal data using protocols consistent with tribal values and worldviews, thus employing CARE. However, these collections are generally not consistent with FAIR principles and require enriched metadata and protocols. The Integrated Data Infrastructure in New Zealand has developed a data access protocol called Ng Tikanga Paihere which is based on Indigenous concepts and values consistent with CARE as well as ensuring the data are also FAIR . Application of CARE with FAIR requires a clear set of criteria and tools such as the FAIR Data Maturity Model. Compiling existing and creating new tools and criteria for implementing the CARE Principles are needed to achieve data that are FAIR with CARE.

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Data With Principles: How To Be Fair And Why You Should Care

This blog post was written by Joe Wright, SFU Library co-op student

The unstoppable Open Access movement is well upon us, making research accessible by removing barriers and increasing shareability. As research publications become more accessible, it raises the question of where this leaves research data.

Why should research data be made accessible, and how does this concern private or protected data? How can you make your data accessible properly, and ethically? Thankfully, there are two essential guiding principles for responsible and inclusive data management to answer these concerns: the FAIR and CARE Data Principles.

Figure 1Depiction of the FAIR and CARE principles

Note. From CARE principles for Indigenous data governance, by Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2013, The Global Indigenous Data Alliance, .

What Does FAIR Data Mean?

The FAIR Guiding Principles for scientific data management and stewardship were published in 2016, a collaboration between academia, industry, funding agencies, and scholarly publishers to create a standard for the accessibility and reusability of data, from both a human and machine perspective. This often relates not only to the data itself, but also to the metadata that comes with it, or the information that gives a description and a context, helping readers to understand and organize it. This includes things like the author, date created, size, and potentially much more.

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Implementation Of Care Principles Guided By Tribal Oversight

The current structures that are in place for federal biomedical data governance, in particular the Common Rule , fail to align with the rights and interests of Indigenous nations and communities . Rather than demanding that representatives of Indigenous communities participate in these existing governance structures, we argue for sovereign controlthat is, Indigenous nations controlling ownership, governing storage, and dictating parameters for data use and reuse. We also promote policy innovations for other institutions that both adhere to tribal sovereignty and protect Indigenous people living off tribal lands or who self-identify as Indigenous .

This section introduces the CARE Principles for Indigenous Data Governance as high-level guidance for enhancing IDSov in research and data governance. This section also examines the sovereign expectations that tribes set for researchers and institutions to support Indigenous Peoples efforts to reclaim control and oversight of data, including biospecimens.

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Indigenous Peoples And Data

For the purposes of this paper, we define Indigenous Peoples in the US as American Indian, Alaska Native, Native Hawaiian, and other communities who are indigenous to the US and its territories. We will use Native nations and tribes interchangeably to refer to tribal nations in the US. The federal government recognizes 574 tribes in the US as sovereign nations with their own legal and political structures to govern their citizens and homelands . In addition, many other Indigenous Peoples exert sovereignty as state-recognized or un-recognized nations, including those in the state of Hawaii and US territories. Sovereignty refers to the collective powers of a nation, such as the power to grant access to the population or to negotiate treaties between nations. As sovereign nations, tribes have the power to govern via their own structures, determine their own citizenship, and regulate tribal business .

IDGov and tribal research governance complement one another: some data are research data that are subject to both data governance and research governance. Thus, Indigenous research governance becomes a mechanism for enhancing IDGov as tribes assert IDSov.

Development Of The Care Principles

The CARE Principles for Indigenous Data Governance

The CARE principles were initially developed at a workshop on Indigenous data sovereignty held in Gaborone, Botswana, as part of International Data Week in 2018. The workshop was Indigenous-led and brought together Indigenous and allied researchers and practitioners to draft principles for Indigenous data governance in support of innovation, governance and self-determination among Indigenous Peoples, nations and communities. The design of and action within the Indigenous data sovereignty movement itself centres leadership by Indigenous Peoples.

The RDA International Indigenous Data Sovereignty Interest Group, the workshop host, embodies this philosophy with Indigenous scholar leaders from three continents and Indigenous scholar group members from six continents. They work in collaboration with allied scholars at RDA from across the globe and from diverse disciplines.11 19

This group emerged as a natural site to host a workshop to co-develop a draft set of principles for the governance of Indigenous data. Understanding the need for input, guidance and feedback from Indigenous leaders, communities, scholars and mainstream data users, an iterative process, collecting comments and edits to the drafts, was selected to encourage broad input and participation.

Figure 1: The CARE principles for Indigenous data governance

Figure 2: Be FAIR and CARE when using Indigenous data

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Datastream And The Fair And Care Principles

Our work at DataStream reflects an ongoing commitment to upholding the FAIR and CARE principles. These principles inform how weve built the technology behind DataStream, how we connect with other data systems and tools, and how we work with data stewards to ensure clarity around data ownership, licensing, and control over what is published on DataStream.

DataStreams Data Policy, Open Data Schema , implementation of dataset DOIs, and integration with other tools and repositories are just some examples of how we are putting these principles into practice to enhance water data management in Canada.

Responsible Data Management Guidelines To Protect Privacy

CGIAR Platform for Big Data in Agriculture advocates open data for agricultural research for development. It considers that opening up research data for scrutiny and reuse confers significant benefits to society.

However, the Platform appreciates that not all research data can be open and that a broad range of legitimate circumstances may require data to be restricted.

As an integral component of its advocacy for open data, the Platform promotes responsible data management through the entire research data lifecycle from planning, collecting, storing, disclosing or publishing, transferring, discovery and archiving.

These guidelines were created from information collected from: review on best and emerging practices across various sectors in the fast changing landscape of privacy and ethics privacy and ethic materials sourced from seven CGIAR centers first draft was circulated for input and feedback across CGIAR and incorporated into this edition. Its important to note that this is an evolving document, the next stage is to consult externally for further input.

These Guidelines are intended to assist agricultural researchers handle privacy and personally identifiable information in the research project data lifecycle.

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Care Principles For Indigenous Data Governance At The American Association For The Advancement Of Science Annual Meeting

On February 18, Andrew Martinez participated in the Data Decision to Action: Public Data Infrastructure for Scientific Discovery panel session during the American Association for the Advancement of Science Annual Meeting. The session centered on the opportunities data generates for scientific discovery, innovation, and building quality public data infrastructures, and consisted of three pre-recorded presentations and a live Q& A panel. Stephanie Russo Carroll contributed to a pre-recorded presentation. Each speaker addressed how data that are Findable, Accessible, Interoperable, and Reusable enhance these opportunities. During the live session, Martinez emphasized that to build quality infrastructures, data must implement Indigenous governance principles that are FAIR and advance Collective Benefit, Authority to Control, Responsibility, and Ethics . Operationalizing CARE results in a more integrated approach to data collection, storage, and use, focus not only on individuals, but also community-controlled data, and find ways to build trust between data actors and tribal rightsholders.

This panel served as a great opportunity to continue to introduce researchers, data scientists, software developers, and others to the CARE Principles for Indigenous Data Governance. Following the panel, Martinez and NNIs Stephanie Russo Carroll were invited to draft a follow up paper.

Fair In The Context Of Indigenous Data

Be FAIR and CARE: The CARE Principles for Indigenous Data Governance

RDA as a truly global organization stands for data sharing without barriers. A significant contribution of RDA is to provide a platform for research communities, including for small, technical, or even sporadic ones. Researchers can participate in discussions at a global level, from their local research environment. Personal contributions on dedicated topics, via collaborative work results in agreed outputs and working methods.

The International Indigenous Data Sovereignty Interest Group, among many activities, created the CARE Principles and now seeks to identify policy and practice to implement the principles across data ecosystems. The CARE Principles, beyond supporting Indigenous Peoples rights and interests, is a new, game changing perspective, stimulating researchers across domains and regions as an effort to articulate community data rights.

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