Data Access and Data Systems

2010 ◽  
pp. 127-143 ◽  
Author(s):  
Ruixin Yang ◽  
Hampapuram Ramapriyan ◽  
Carol Meyer
Keyword(s):  
Data Access ◽  
Data Systems

Data Inter-Operability Driven by Oceanic Data Assimilation Needs

1999 ◽  
Vol 33 (3) ◽  
pp. 55-66 ◽  
Author(s):  
L. Charles Sun
Keyword(s):  
Data Assimilation ◽  
Data Access ◽  
Heterogeneous Data ◽  
Environmental Data ◽  
Ocean Dynamics ◽  
Data Systems ◽  
Oceanographic Data ◽  
Ease Of Access ◽  
Access To Data ◽  
Station Location

An interactive data access and retrieval system, developed at the U.S. National Oceanographic Data Genter (NODG) and available at <ext-link ext-link-type="uri" href="http://www.node.noaa.gov">http://www.node.noaa.gov</ext-link>, is presented in this paper. The purposes of this paper are: (1) to illustrate the procedures of quality control and loading oceanographic data into the NODG ocean databases and (2) to describe the development of a system to manage, visualize, and disseminate the NODG data holdings over the Internet. The objective of the system is to provide ease of access to data that will be required for data assimilation models. With advances in scientific understanding of the ocean dynamics, data assimilation models require the synthesis of data from a variety of resources. Modern intelligent data systems usually involve integrating distributed heterogeneous data and information sources. As the repository for oceanographic data, NOAA’s National Oceanographic Data Genter (NODG) is in a unique position to develop such a data system. In support of the data assimilation needs, NODG has developed a system to facilitate browsing of the oceanographic environmental data and information that is available on-line at NODG. Users may select oceanographic data based on geographic areas, time periods and measured parameters. Once the selection is complete, users may produce a station location plot, produce plots of the parameters or retrieve the data.

Managing Inconsistencies in Data Grid Environments

2012 ◽  
pp. 303-316
Author(s):  
Ejaz Ahmed ◽  
Nik Bessis ◽  
Peter Norrington ◽  
Yong Yue
Keyword(s):  
Data Access ◽  
Heterogeneous Data ◽  
Data Mapping ◽  
Data Systems ◽  
Grid Environment ◽  
Federated Databases ◽  
Matching Process ◽  
Generic Strategy ◽  
Service Oriented ◽  
Matching Strategy

Much work has been done in the area of data access and integration using various data mapping, matching, and loading techniques. One of the main concerns when integrating data from heterogeneous data sources is data redundancy. The concern is mainly due to the different business contexts and purposes from which the data systems were originally built. A common process for accessing data from integrated databases involves the use of each data source’s own catalogue or metadata schema. In this article, the authors take the view that there is a greater chance of data inconsistencies, such as data redundancies when integrating them within a grid environment as compared to traditional distributed paradigms. The importance of improving the data search and matching process is briefly discussed, and a partial service oriented generic strategy is adopted to consolidate distinct catalogue schemas of federated databases to access information seamlessly. To this end, a proposed matching strategy between structure objects and data values across federated databases in a grid environment is presented.

Managing Inconsistencies in Data Grid Environments

2010 ◽  
Vol 2 (4) ◽  
pp. 51-64 ◽  
Author(s):  
Ejaz Ahmed ◽  
Nik Bessis ◽  
Peter Norrington ◽  
Yong Yue
Keyword(s):  
Data Access ◽  
Heterogeneous Data ◽  
Data Mapping ◽  
Data Systems ◽  
Grid Environment ◽  
Federated Databases ◽  
Matching Process ◽  
Generic Strategy ◽  
Service Oriented ◽  
Matching Strategy

Much work has been done in the area of data access and integration using various data mapping, matching, and loading techniques. One of the main concerns when integrating data from heterogeneous data sources is data redundancy. The concern is mainly due to the different business contexts and purposes from which the data systems were originally built. A common process for accessing data from integrated databases involves the use of each data source’s own catalogue or metadata schema. In this article, the authors take the view that there is a greater chance of data inconsistencies, such as data redundancies when integrating them within a grid environment as compared to traditional distributed paradigms. The importance of improving the data search and matching process is briefly discussed, and a partial service oriented generic strategy is adopted to consolidate distinct catalogue schemas of federated databases to access information seamlessly. To this end, a proposed matching strategy between structure objects and data values across federated databases in a grid environment is presented.

scholarly journals Centering Equity Across the Administrative Data Life Cycle

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Amy Hawn Nelson
Keyword(s):  
Life Cycle ◽  
Data Integration ◽  
Administrative Data ◽  
Data Access ◽  
Data Reuse ◽  
Risk Modeling ◽  
Data Systems ◽  
The Public ◽  
Data Life Cycle ◽  
The Us

Data integration by local and state governments is undertaken for the public good to support the interconnected needs of families and communities. And though data infrastructure is a powerful tool to support equity-oriented reforms, equity is rarely centered as a core goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling. This session presents findings from a toolkit collaboratively generated by a workgroup convened by Actionable Intelligence for Social Policy at the University of Pennsylvania. IntroductionWhile data sharing occurs within a legal framework, an emphasis on equity is often peripheral. Generally, institutions have not adequately examined and acknowledged structural bias in their history, or the ways in which data reflects systemic inequities in the development and administration of policies and programs. Meanwhile, the public are rarely consulted in the development and use of data systems. Objectives and ApproachThis toolkit was collaboratively generated by a workgroup of civic data stakeholders from across the US. ResultsThe toolkit aims to support agencies seeking to acknowledge and compensate for the harms and bias baked into data and practice. It is organized across six stages of the administrative data life cycle—planning, data collection, data access, use of algorithms and statistical tools, analysis, and reporting and dissemination. For each stage, the toolkit includes promising and problematic practices for centering equity in administrative data reuse, with site-based examples of work in action from across the US. Conclusion / ImplicationsThe workgroup concluded that centering equity within data integration efforts is not a binary outcome, but rather a series of small steps towards more equitable practice. There are countless ways to center equity across the administrative data reuse life cycle, and this report provides concrete strategies for agencies, organizations and collaboratives to begin and grow that work in practice.

scholarly journals Strategies for centering equity and public engagement in the ethical use of integrated administrative data

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Adelia Jenkins ◽  
Amy Hawn Nelson
Keyword(s):  
Data Integration ◽  
Administrative Data ◽  
Public Engagement ◽  
Data Access ◽  
The United States ◽  
Extensive Literature ◽  
Risk Modeling ◽  
Data Systems ◽  
Data Infrastructure ◽  
The Public

Background with rationaleData integration is undertaken for the public good, yet institutions rarely address structural bias in their history, or the ways data are biased due to systemic inequities in the administration of policies and programs. Meanwhile, the public are rarely consulted in data use. Though data infrastructure can be a powerful tool to support equity-oriented reforms, equity is rarely a stated goal for data integration. This raises fundamental concerns, as integrated data increasingly provide the raw materials for evaluation, research, and risk modeling that inform policy, practice, and resource allocation. Actionable Intelligence for Social Policy (AISP) is an initiative of the University of Pennsylvania that focuses on the development, use, and innovation of integrated data systems (IDS). We convene a network of IDS across the United States while supporting developing sites, and as such are uniquely situated to convene experts to develop guidance for centering equity within integrated data infrastructure. Main AimThis project aims to generate guidance for agencies supporting data sharing infrastructure to ensure an emphasis on equity and public engagement for ethical use. Methods/ApproachA variety of data collection methods are being used, including expert panel convenings and interviews with sites piloting or exemplifying strategies for public engagement and equitable data access and use. An extensive literature review is also in progress and will inform a suite of forthcoming products, including a white paper, communications and training materials. ResultsThe results will provide strategies for centering equity across the spectrum of data integration activities, including inclusive governance, staffing considerations, decisions about data quality, and the ethical use of data models and algorithms. Initial findings indicate there are few exemplar sites that routinely center equity within data integration efforts, yet there are promising incremental steps that sites can take to ensure ethical use. ConclusionWhile centering equity within data integration is an emerging focus, initial findings indicate the importance of such efforts, particularly in acknowledging and mitigating the risks of unacknowledged bias across use of administrative data for research and evaluation purposes.

scholarly journals Building a Canadian Data Platform under the Strategy for Patient-Oriented Research

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Kimberlyn McGrail
Keyword(s):  
Social Services ◽  
Data Access ◽  
Indigenous Communities ◽  
Data Systems ◽  
Natural Experiments ◽  
Process Data ◽  
Canadian Data ◽  
Data Platform ◽  
Access To Data ◽  
Data Documentation

Background with rationaleThe diversity of Canada’s health systems and policies offers fertile ground for natural experiments, comparative analysis, and sharing of best practices. Investments over the last 25 years, measured in hundreds of millions of dollars, have created provincial centres with rich health and social data, national health surveys and more recently, clinical and other data. While much progress has been made within individual provinces and territories, challenges with comparability and timely access to data between jurisdictions remain. Main AimOur aim is to develop a distributed network that facilitates and accelerates multi-jurisdictional research. Methods/ApproachOur team includes data stewards, clinicians, decision-makers, patients, and researchers who are recognized as international leaders in data systems, access governance and engagement. The objectives for the Canadian Data Platform are to: create a data access support system that helps navigate multi-jurisdiction requests; to harmonize and validate definitions for important chronic diseases and other key variables to facilitate multi-jurisdictional research; to continue to expand the sources and types of data and linkages available; to develop the technology infrastructure required to improve the data access request process, data documentation, and re-use of algorithms; to create supports for advanced analytics and infrastructure for data collection and analysis; to establish strong partnerships with patients and the public and with Indigenous communities; and to build strong governance and enable national coordination. ResultsOur Data Access Support Hub will open in the fall of 2019, at which time we will have an inventory of data available across our network, and the beginnings of a catalog of algorithms and harmonized data. ConclusionBuilding cross-national resources to support multi-jurisdictional research can be challenging in places where there are multiple levels of governance of health and social services. Our network is one example of an approach to addressing these challenges.

Bridging the gap between Big Earth data users and future (cloud-based) data systems - Towards a better understanding of user requirements of cloud-based data systems

2020 ◽  
Author(s):  
Julia Wagemann ◽  
Stephan Siemen ◽  
Jörg Bendix ◽  
Bernhard Seeger
Keyword(s):  
Data Access ◽  
Google Earth ◽  
Earth Observation ◽  
Environmental Data ◽  
User Requirements ◽  
Observation Data ◽  
Data Systems ◽  
Data Services ◽  
Study Results ◽  
The Future

&lt;p&gt;The European Commission&amp;#8217;s Earth Observation programme Copernicus produces an unprecedented amount of openly available multi-dimensional environmental data. However, data &amp;#8216;accessibility&amp;#8217; remains one of the biggest obstacles for users of open Big Earth Data and hinders full data exploitation. Data services have to evolve from pure download services to offer an easier and more on-demand data access. There are currently different concepts explored to make Big Earth Data better accessible for users, e.g. virtual research infrastructures, data cube technologies, standardised web services or cloud processing services, such as the Google Earth Engine or the Copernicus Climate Data Store Toolbox. Each offering provides different types of data, tools and functionalities. Data services are often developed solely satisfying specific user requirements and needs.&lt;/p&gt;&lt;p&gt;For this reason, we conducted a user requirements survey between November 2018 and June 2019 among users of Big Earth Data (including users of Earth Observation data, meteorological and environmental forecasts and other geospatial data) to better understand user requirements of Big Earth Data. To reach an active data user community for this survey, we partnered with ECMWF, which has 40 years of experience in providing data services for weather forecast data and environmental data sets of the Copernicus Programme.&lt;/p&gt;&lt;p&gt;We were interested in which datasets users currently use, which datasets they would like to use in the future and the reasons why they have not yet explored certain datasets. We were interested in the tools and software they use to process the data and what challenges they face in accessing and handling Big Earth Data. Another part focused on future (cloud-based) data services and there, we were interested in the users&amp;#8217; motivation to migrate their data processing tasks to cloud-based data services and asked them what aspects of these services they consider being important.&lt;/p&gt;&lt;p&gt;While preliminary results of the study were released last year, this year the final study results are presented. A specific focus will be put on users&amp;#8217; expectation of future (cloud-based) data services aligned with recommendations for data users and data providers alike to ensure the full exploitation of Big Earth Data in the future.&lt;/p&gt;

scholarly journals Framework for Centering Racial Equity Throughout the Administrative Data Life Cycle

2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Amy L Hawn Nelson ◽  
Sharon Zanti
Keyword(s):  
Life Cycle ◽  
Data Integration ◽  
Data Access ◽  
Risk Modeling ◽  
Data Systems ◽  
Racial Equity ◽  
Data Infrastructure ◽  
The Public ◽  
Data Life Cycle ◽  
The U.S

IntroductionData integration by local and state governments is undertaken for the public good to support theinterconnected needs of families and communities. Though data infrastructure is a powerful toolto support equity-oriented reforms, racial equity is rarely centered or prioritized as a core goalfor data integration. This raises fundamental concerns, as integrated data increasingly provide theraw materials for evaluation, research, and risk modeling. Generally, institutions have not adequatelyexamined and acknowledged structural bias in their history, or the ways in which data reflect systemicracial inequities in the development and administration of policies and programs. Meanwhile, civicdata users and the public are rarely consulted in the development and use of data systems ObjectivesThis paper presents a framework and site-based examples of “Work in Action” that werecollaboratively generated by a civic data stakeholder workgroup from across the U.S. in 2019–2020. MethodsPurposive sampling was used to curate a diverse 15-person workgroup that used participatory actionresearch and public deliberation to co-create a framework of best practices. ResultsThis framework aims to support agencies seeking to acknowledge and compensate for the harmsand bias baked into data and practice. It is organized across six stages of the administrativedata life cycle—planning, data collection, data access, use of algorithms/statistical tools, analysis,and reporting and dissemination. For each stage, the framework includes positive and problematicpractices for centering racial equity, with site-based examples of “Work in Action” from across theU.S. Using this framework, the workgroup then developed a Toolkit for Centering Racial EquityThroughout Data Integration, a resource that has been broadly disseminated across the U.S. ConclusionsFindings indicate that centering racial equity within data integration efforts is not a binary outcome,but rather a series of small steps towards more equitable practice. There are countless ways tocenter racial equity across the data life cycle, and this framework provides concrete strategies fororganizations to begin to grow that work in practice.

SIOS Data Management System: distributed data system for Earth System Science

2021 ◽  
Author(s):  
Dariusz Ignatiuk ◽  
Øystein Godøy ◽  
Lara Ferrighi ◽  
Inger Jennings ◽  
Christiane Hübner ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Data Access ◽  
Data Management System ◽  
Data Series ◽  
Data Systems ◽  
Earth System ◽  
Data Repositories ◽  
Earth System Science ◽  
System Science

&lt;p&gt;Svalbard Integrated Arctic Earth Observing System (SIOS) is an international consortium to develop and maintain a regional observing system in Svalbard and the associated waters. SIOS brings together the existing infrastructure and data of its members into a multidisciplinary network dedicated to answering Earth System Science (ESS) questions related to global change. The Observing System is built around &amp;#8220;SIOS core data&amp;#8221; &amp;#8211; long-term data series collected by SIOS partners. SIOS Data Management System (SDMS) is dedicated to harvesting information on historical and current datasets from collaborating thematic and institutional data centres and making them available to users. A central data access portal is linked to the data repositories maintained by SIOS partners, which manage and distribute data sets and their associated metadata. The integrity of the information and harmonisation of data is based on internationally accepted protocols assuring interoperability of data, standardised documentation of data through the use of metadata and standardised interfaces by data systems through the discovery of metadata. By these means, SDMS is working towards FAIR data compliance (making data findable, accessible, interoperable and reusable), among other initiatives through the H2020 funded ENVRI-FAIR project (http://envri.eu/envri-fair/).&lt;/p&gt;

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