Managing scientific data for long-term access and use

Preservation of data for long-term use will require data management strategies that include curation and preservation planning and implementation. While data management and curatorial activities have been an integral part of some scientific domains for years (see for example, high energy particle physics), these are new concepts in other areas of science. Concepts such as provenance, representation for re-use, and work-flow capture are rarely understood, let alone addressed. By bringing together theories and best practices from archives, museum studies, and library and information science (LIS), it is possible to address these problems. on current research into scientific data management problems, this panel will consider questions about sharing and re-use of data, curation and preservation, and the intersection of scientific production and scholarly communication. Our research explores information work and problems across a range of scientific areas in the life and physical sciences, including genomics, neuroscience, ecology, and earth science. As more scientific work products are shifted to open or shared data collections (including archives, repositories and databases), we will need to understand how these systems are implemented and used to support collaboration and discovery, as well as scholarly and scientific communication.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57315/1/14504301123_ftp.pd

Data Sharing Frames: How Scientists Understand the Work of Sharing Scientific Data

The curation of data is fundamental to their wider dissemination and use. This paper investigates the frames of workers who perform data curation in scientific contexts. We view data curation as a sense-making practice, where workers collaborate to disseminate meaningful data to a broad set of prospective users. Previous Information Systems investigations have suggested that data-related activities are dependent on workers’ understanding of their local work context. We expand this with an evolving and long-term view. We use a stepwise-deductive induction method to examine how scientists understand the work involved in curating scientific data for public sharing. We draw on frames as the theoretical lens of the study that enables us to identify three data sharing frames – the object, curation, and aligning frames – as important frames that shape how scientists curate data for public sharing. Our analysis provides a deeper understanding of the nuances of managing scientific data for public access. Our main contribution is the articulation of an evolving and long-term view of how workers approach their tasks in getting data ready for long-term public use

Comparing Approaches for the Sustainability of Scientific Data Repositories

Sustainable data systems are critical components of the cyberinfrastructure needed to provide long-term stewardship of scientific data, including Earth science data, throughout their entire life cycle. A variety of approaches may help ensure the sustainability of such systems, but these approaches must be able to survive the demands of competing priorities and decreasing budgets over long time periods. Analyzing and comparing various approaches can identify viable aspects of each approach and inform decisions for developing, managing, and supporting the cyberinfrastructure needed to facilitate discovery, access, and analysis of data by future communities of users. A typology of sustainability approaches is proposed, and example use cases are offered for assessing the approaches over time. These examples help illustrate the potential strengths and weaknesses of each approach under various conditions and with regard to different objectives, e.g., open vs. limited access. By applying the results of these analyses to their particular circumstances, systems stakeholders can assess their options for a sustainable systems approach, which may incorporate multiple sustainability options, along with other metrics to ensure the sustainability of the scientific data and information for which they are responsible. In addition, clarifying and comparing sustainability approaches should inform the design of new systems and the improvement of existing systems to meet the needs for long-term stewardship of scientific data, and support education and workforce development efforts needed to ensure that the appropriate scientific and technical skills are available to operate and further develop sustainable cyberinfrastructure

EXECUTABLE ARCHIVES: Software integrity for data readability and validation of archived studies

© 2021 author(s). The text of this paper is published under a CC-BY license (https://creativecommons.org/licenses/by/4.0/)This paper presents practices and processes for managing software integrity to support data archiving for long term use in response to the regulatory requirements. Through a case study of a scientific software decommissioning, we revisit the issues of archived data readability. Established software lifecycle management processes are extended with archiving and data integrity requirements for retention of data and revalidation of data analyses. That includes the software transition from operational to archival use within the Executable Archive model that extends the traditional data archive with computing environments with software installations required to reproduce study results from the archived records. The content use requirements are an integral part of both data access and the software management considerations, assuring that data integrity is fully supported by the software integrityPeer reviewe

Trust threads: minimal provenance and data publication and reuse

Presented at the National data integrity conference: enabling research: new challenges & opportunities held on May 7-8, 2015 at Colorado State University, Fort Collins, Colorado. Researchers, administrators and integrity officers are encountering new challenges regarding research data and integrity. This conference aims to provide attendees with both a high level understanding of these challenges and impart practical tools and skills to deal with them. Topics will include data reproducibility, validity, privacy, security, visualization, reuse, access, preservation, rights and management.Beth A. Plale is the Director, Data to Insight Center, Managing Director, Pervasive Technology Institute and a Professor, School of Informatics and Computing Indiana University. Dr. Plale has broad research and governance interest in information, in long-term preservation and access to scientific data, and in enabling computational access to large and complex data for broader use. Her specific research interest are in metadata and data provenance, trusted data repositories and enclaves, data analysis and text mining of big data, and workflow systems. Plale teaches in the Data Science Program at Indiana University Bloomington. She is deeply engaged in interdisciplinary research and education and has substantive experience in developing stable and useable scientific cyberinfrastructure.PowerPoint presentation given on May 8, 2015

The TRY Database System

The TRY initiative (www.try-db.org) is a network of vegetation scientists providing curated plant trait data for the scientific community. The TRY Database currently contains about 7 million trait records for nearly 3000 different traits. The flexible database structure can hold any number of traits and a generic program can import any kind of data without requiring a template. About 10 million trait records for about 100 requests are released on a monthly basis. This is organized via the TRY Data Portal, which facilitates data contribution, exploration and customized requests. The Dataset Custodian Centre allows managing the status of contributed datasets and monitoring the use of these data from requests to scientific publications. The Request PI Centre allows managing and monitoring requests. Both centres facilitate direct contact of data contributors and users. In addition to the TRY Database we have established a file archive, which facilitates publication and DOIs for else unpublished plant trait datasets. The TRY Data Portal has evolved toward a long-term scientific data infrastructure, which combines the advantages of easy access to curated plant trait data almost ready for analyses, with direct contact of data providers and users, the opportunity for data providers to publish individual datasets and track the use of their data. This presentation will introduce details of the TRY database system

Data Management in the Long Tail: Science, Software, and Service

Scientists in all fields face challenges in managing and sustaining access to their research data. The larger and longer term the research project, the more likely that scientists are to have resources and dedicated staff to manage their technology and data, leaving those scientists whose work is based on smaller and shorter term projects at a disadvantage. The volume and variety of data to be managed varies by many factors, only two of which are the number of collaborators and length of the project. As part of an NSF project to conceptualize the Institute for Empowering Long Tail Research, we explored opportunities offered by Software as a Service (SaaS). These cloud-based services are popular in business because they reduce costs and labor for technology management, and are gaining ground in scientific environments for similar reasons. We studied three settings where scientists conduct research in small and medium-sized laboratories. Two were NSF Science and Technology Centers (CENS and C-DEBI) and the third was a workshop of natural reserve scientists and managers. These laboratories have highly diverse data and practices, make minimal use of standards for data or metadata, and lack resources for data management or sustaining access to their data, despite recognizing the need. We found that SaaS could address technical needs for basic document creation, analysis, and storage, but did not support the diverse and rapidly changing needs for sophisticated domain-specific tools and services. These are much more challenging knowledge infrastructure requirements that require long-term investments by multiple stakeholders.

Guidance note on the application of coastal monitoring for small island developing states : Part of the NOC-led project “Climate Change Impact Assessment: Ocean Modelling and Monitoring for the Caribbean CME states”, 2017-2020; under the Commonwealth Marine Economies (CME) Programme in the Caribbean.

Small Island Developing States (SIDS) are a diverse group of 51 countries and territories vulnerable to human-induced climate change, due to factors including their small size, large exclusive economic zones and limited resources. They generally have insufficient critical mass in scientific research and technical capability to carry out coastal monitoring campaigns from scratch and limited access to data. This guidance report will go some way to addressing these issues by providing information on monitoring methods and signposting data sources. Coastal monitoring, the collection, analysis and storage of information about coastal processes and the response of the coastline, provides information on how the coast changes over time, after storm events and due to the effects of human intervention. Accurate and repeatable observational data is essential to informed decision making, particularly in light of climate change, the impacts of which are already being felt. In this report, we review the need for monitoring and the development of appropriate strategies, which include good baseline data and long-term repeatable data collection at appropriate timescales. We identify some of the methods for collection of in situ data, such as tide gauges and topographic survey, and highlight where resources in terms of data and equipment are currently available. We then go on to explore the range of remote sensing methods available from satellites to smart phone photography. Both in situ and remotely sensed data are important as inputs into models, which in turn feed in to visualisations for decision-making. We review the availability of a wide range of datasets, including details of how to access satellite data and links to international and regional data banks. The report concludes with information on the use of Geographical Information Systems (GIS) and good practice in managing data

Data management for NREL and beyond: a roadmap and recommendations

The SGS-LTER research site was established in 1980 by researchers at Colorado State University as part of a network of long-term research sites within the US LTER Network, supported by the National Science Foundation. Scientists within the Natural Resource Ecology Lab, Department of Forest and Rangeland Stewardship, Department of Soil and Crop Sciences, and Biology Department at CSU, California State Fullerton, USDA Agricultural Research Service, University of Northern Colorado, and the University of Wyoming, among others, have contributed to our understanding of the structure and functions of the shortgrass steppe and other diverse ecosystems across the network while maintaining a common mission and sharing expertise, data and infrastructure.Version 1.1 - June 2013.Includes bibliographical references.This report contains a summary of activities lead by Nicole Kaplan, RA, and Greg Newman, Research Scientist, and a strategy for managing data, which were supported by 2012 Program Development Funds at the Natural Resource Ecology Laboratory (NREL). We set out to describe current components, functions and expertise of the NREL cyber-infrastructure and inquire about current work and future needs for managing, archiving and providing access to data. The activities we coordinated included: (1) conducting a workshop, (2) arranging the Spring 2012 seminar series entitled Data Literacy: Bridging the Gap Between Science and Society, (3) contributing to over a dozen data management plans for NREL proposals, (4) providing database services to PHACE (a large collaborative project), and (5) attending conferences and workshops to obtain new ideas and build partnerships. The results of what we learned and accomplished include overall data management goals for NREL, a model for a data management system for NREL, requirements for a web-based data access and delivery system, policies for data sharing and attribution, training for undergraduate and graduate students, and scholarly work in Ecological Informatics (e.g. Chu et al. in press, Newman et al. 2011, Vanderbilt et al. 2009). Our work was presented at the NREL 2012 Annual Retreat and the presentation file is available upon request. Here, we present resources for NREL to plan and conduct data management as part of the research process. We propose a model that establishes a hierarchy of workspaces to support, preserve, and secure the flow of data from the private laboratory space of the Principal Investigator, to a web-based NREL Data Repository to disseminate data to the public and stakeholders. We detail how current cyber-infrastructure capabilities can be leveraged to meet needs within NREL for data management, facilitate research discovery and data re-use, and contribute to NREL excellence. Major benefits to managing, sharing and re-using data include increasing the competitiveness of NREL proposals, influencing expectations of funding agencies for data management, accelerating global change research (Wolkovich et al. 2012), and broadening scientific understanding and services to support decision-making (Dozier and Gail 2009). In addition, federal research sponsors are committed to increasing open access to data and are requiring data management plans from agencies and departments with over 100 million dollars in research and development expenditures (Holdren 2013 and e.g. The Fair Access to Science and Technology Research Act (FASTR).Supported by 2012 NREL Program Development Fund

Towards Sustainable Documentation of Geographical Names of Touristic and Heritage Sites in Occupied Jerusalem Using Geographical Information System (GIS)

Geographical Information System (GIS) is an important tool in documenting and managing archaeological and heritage sites in the city of Jerusalem in order to benefit from its high capabilities in monitoring, documentation, analysis, presentation and other capabilities required by the documentation of archaeological and heritage sites in Jerusalem with their Arabic names, which deal with large amounts of spatial and descriptive data, also to maximize the use of spatial data collected on archaeological and heritage sites in Jerusalem, and the conversion of geographical databases electronically, which allows more than one user or management access to data and modifying it simultaneously, which saves a lot of time, effort and cost in medium to long term, giving later a wide horizon for disseminating data and designing practical applications, and contributing to the electronic and comprehensive documentation processes of archaeological and heritage sites in the city of Jerusalem in a sustainable manner. The study has concluded that it is important to adopt modern technology systems in documenting Arabic names of heritage sites in order to preserve these names for future generations and to resist the Judaization and preservation of these names over time. Moreover, this study recommended to adopt the proposed documentation system and to expand the processes of building electronic databases to Jerusalem and other Palestinian cities in order to sustainably protect it based on scientific foundations that contribute in providing the electronic database. Keywords: Geographic Information System (GIS), Spatial Decision Support System (SDSS), Logical Model, Physical Model, (RS) Remote Sensing), occupied Jerusalem.