After a century of theorising and applying management practices, we are in the middle of entering a new stage in management science: digital management. The management of digital data submerges in traditional functions of management and, at the same time, continues to recreate viable solutions and conceptualisations in its established fields, e.g. research data management. Yet, one can observe bilateral synergies and mutual enrichment of traditional and data management practices in all fields. The paper at hand addresses a case in point, in which new and old management practices amalgamate to meet a steadily, in part characterised by leaps and bounds, increasing demand of data curation services in academic institutions. The idea of modularisation, as known from software engineering, is applied to data curation workflows so that economies of scale and scope can be used. While scaling refers to both management science and data science, optimising is understood in the traditional managerial sense, that is, with respect to the cost function. By means of a situation analysis describing how data curation services were applied from one department to the entire institution and an analysis of the factors of influence, a method of modularisation is outlined that converges to an optimal state of curation workflows.
{"title":"Scaling by Optimising: Modularisation of Data Curation Services in Growing Organisations","authors":"Hagen Peukert","doi":"10.2218/ijdc.v16i1.650","DOIUrl":"https://doi.org/10.2218/ijdc.v16i1.650","url":null,"abstract":"After a century of theorising and applying management practices, we are in the middle of entering a new stage in management science: digital management. The management of digital data submerges in traditional functions of management and, at the same time, continues to recreate viable solutions and conceptualisations in its established fields, e.g. research data management. Yet, one can observe bilateral synergies and mutual enrichment of traditional and data management practices in all fields. The paper at hand addresses a case in point, in which new and old management practices amalgamate to meet a steadily, in part characterised by leaps and bounds, increasing demand of data curation services in academic institutions. The idea of modularisation, as known from software engineering, is applied to data curation workflows so that economies of scale and scope can be used. While scaling refers to both management science and data science, optimising is understood in the traditional managerial sense, that is, with respect to the cost function. By means of a situation analysis describing how data curation services were applied from one department to the entire institution and an analysis of the factors of influence, a method of modularisation is outlined that converges to an optimal state of curation workflows.","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78747152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert J. Sandusky, Suzie L. Allard, Lynn Baird, L. Cannon, Kevin Crowston, Amy Forrester, Bruce Grant, Rachael Hu, R. Olendorf, Danielle Pollock, A. Specht, C. Tenopir, Rachel Volentine
DataONE, funded from 2009-2019 by the U.S. National Science Foundation, is an early example of a large-scale project that built both a cyberinfrastructure and culture of data discovery, sharing, and reuse. DataONE used a Working Group model, where a diverse group of participants collaborated on targeted research and development activities to achieve broader project goals. This article summarizes the work carried out by two of DataONE’s working groups: Usability & Assessment (2009-2019) and Sociocultural Issues (2009-2014). The activities of these working groups provide a unique longitudinal look at how scientists, librarians, and other key stakeholders engaged in convergence research to identify and analyze practices around research data management through the development of boundary objects, an iterative assessment program, and reflection. Members of the working groups disseminated their findings widely in papers, presentations, and datasets, reaching international audiences through publications in 25 different journals and presentations to over 5,000 people at interdisciplinary venues. The working groups helped inform the DataONE cyberinfrastructure and influenced the evolving data management landscape. By studying working groups over time, the paper also presents lessons learned about the working group model for global large-scale projects that bring together participants from multiple disciplines and communities in convergence research.
{"title":"Assessment, Usability, and Sociocultural Impacts of DataONE","authors":"Robert J. Sandusky, Suzie L. Allard, Lynn Baird, L. Cannon, Kevin Crowston, Amy Forrester, Bruce Grant, Rachael Hu, R. Olendorf, Danielle Pollock, A. Specht, C. Tenopir, Rachel Volentine","doi":"10.2218/ijdc.v16i1.678","DOIUrl":"https://doi.org/10.2218/ijdc.v16i1.678","url":null,"abstract":"DataONE, funded from 2009-2019 by the U.S. National Science Foundation, is an early example of a large-scale project that built both a cyberinfrastructure and culture of data discovery, sharing, and reuse. DataONE used a Working Group model, where a diverse group of participants collaborated on targeted research and development activities to achieve broader project goals. This article summarizes the work carried out by two of DataONE’s working groups: Usability & Assessment (2009-2019) and Sociocultural Issues (2009-2014). The activities of these working groups provide a unique longitudinal look at how scientists, librarians, and other key stakeholders engaged in convergence research to identify and analyze practices around research data management through the development of boundary objects, an iterative assessment program, and reflection. Members of the working groups disseminated their findings widely in papers, presentations, and datasets, reaching international audiences through publications in 25 different journals and presentations to over 5,000 people at interdisciplinary venues. The working groups helped inform the DataONE cyberinfrastructure and influenced the evolving data management landscape. By studying working groups over time, the paper also presents lessons learned about the working group model for global large-scale projects that bring together participants from multiple disciplines and communities in convergence research.","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79944018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we outline the process of revising data access categories for research data sets in GESIS – a large European social science data archive based in Germany. The challenge is to create a minimal set of workable access conditions that cope with a) facilitating as “open as possible, closed as necessary” expectations for data reuse; b) map on to existing legacy access categories and conditions in a data archive. The paper covers the work done in gathering data on data access categories used by data archives in their existing data catalogues, the choices offered to depositors of data in their user agreements, and work done by other data reuse platforms in categorising access to their data. Finally, we talk through the process of refining a minimal set of data access conditions for the GESIS data archive.
{"title":"Access Some Areas: Reforming Access Categories for Data in a Social Science Data Archive","authors":"Laurence Horton, Anja Perry","doi":"10.2218/ijdc.v15i1.708","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.708","url":null,"abstract":"In this paper we outline the process of revising data access categories for research data sets in GESIS – a large European social science data archive based in Germany. The challenge is to create a minimal set of workable access conditions that cope with a) facilitating as “open as possible, closed as necessary” expectations for data reuse; b) map on to existing legacy access categories and conditions in a data archive. \u0000The paper covers the work done in gathering data on data access categories used by data archives in their existing data catalogues, the choices offered to depositors of data in their user agreements, and work done by other data reuse platforms in categorising access to their data. Finally, we talk through the process of refining a minimal set of data access conditions for the GESIS data archive. \u0000 ","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80827368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sound preservation practice is a series of active engagements with the content one hopes to preserve. In many cases, this has not always been the case. Both institutions and services—while not actively encouraging passive preservation—neglect the key components in the stewardship of our historical record. In other words, there is much more to preservation than simply choosing a storage solution and placing one’s content there. The materials need to be verified, checked, and tested against expectations within the service. This is accepted practice for many. However, very few services provide the necessary assurance to test both its own user expectations as well as the depositors’ themselves. Creating a methodology for both depositor and service to be assured that preservation meets expectations is critical. This is happening in very select ways. This paper discusses one such dialogue and its function.
{"title":"Mutually Assured Preservation: Fostering Active Preservation Practice through Fire Drills","authors":"Bradley J. Daigle","doi":"10.2218/ijdc.v15i1.724","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.724","url":null,"abstract":"Sound preservation practice is a series of active engagements with the content one hopes to preserve. In many cases, this has not always been the case. Both institutions and services—while not actively encouraging passive preservation—neglect the key components in the stewardship of our historical record. In other words, there is much more to preservation than simply choosing a storage solution and placing one’s content there. The materials need to be verified, checked, and tested against expectations within the service. This is accepted practice for many. However, very few services provide the necessary assurance to test both its own user expectations as well as the depositors’ themselves. Creating a methodology for both depositor and service to be assured that preservation meets expectations is critical. This is happening in very select ways. This paper discusses one such dialogue and its function. \u0000 ","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74885925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the advent of digital scholarship in the humanities, decades of extensive, distributed scholarly efforts have produced a digital scholarly record that is increasingly scattered, heterogeneous, and independent of curatorial institutions. Digital scholarship produces collections with unique scholarly and cultural value—collections that serve as hubs for collaboration and communication, engage broad audiences, and support new research. Yet, lacking systematic support for digital scholarship in libraries, digital humanities collections are facing a widespread crisis of sustainability. This paper provides outcomes of a multimodal study of sustainability challenges confronting digital collections in the humanities, characterizing institutional and community-oriented strategies for sustaining collections. Strategies that prioritize community engagement with collections and the maintenance of sociotechnical workflows suggest possibilities for novel approaches to collaborative, community-centred sustainability for digital humanities collections.
{"title":"Sustaining Digital Humanities Collections: Challenges and Community-Centred Strategies","authors":"Katrina Fenlon","doi":"10.2218/ijdc.v15i1.725","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.725","url":null,"abstract":"\u0000Since the advent of digital scholarship in the humanities, decades of extensive, distributed scholarly efforts have produced a digital scholarly record that is increasingly scattered, heterogeneous, and independent of curatorial institutions. Digital scholarship produces collections with unique scholarly and cultural value—collections that serve as hubs for collaboration and communication, engage broad audiences, and support new research. Yet, lacking systematic support for digital scholarship in libraries, digital humanities collections are facing a widespread crisis of sustainability. This paper provides outcomes of a multimodal study of sustainability challenges confronting digital collections in the humanities, characterizing institutional and community-oriented strategies for sustaining collections. Strategies that prioritize community engagement with collections and the maintenance of sociotechnical workflows suggest possibilities for novel approaches to collaborative, community-centred sustainability for digital humanities collections. \u0000","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75948954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Albani, Iolanda Maggio, Ceos Data Stewardship Interest Group
Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community. The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle.
{"title":"Long-Term Data Preservation Data Lifecycle, Standardisation Process, Implementation and Lessons Learned","authors":"M. Albani, Iolanda Maggio, Ceos Data Stewardship Interest Group","doi":"10.2218/ijdc.v15i1.715","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.715","url":null,"abstract":"Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community. \u0000The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle.","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89236660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores Research Data Management (RDM) at the University of Ghana (UG). It emphasises on institutional awareness and attitudes, and whether the University Library is officially supporting this emerging strategic interest in research focused Higher Education Institutions (HEIs). Purposive sampling was used to select information-rich respondents from across the University (i.e. Librarians, Research Administrators, ICT Managers and Senior Researchers) who were interviewed on a range of issues about RDM. Institutional documents were also reviewed to corroborate the primary data and get a deeper understanding of the research problem. The study shows that while RDM is recognised at the institutional level as good research practice and integrity issue, the concept is tenuously understood in the local community. Unsurprisingly, however, there was a general appreciation and awareness of the need for RDM and the implications for such critical concerns as security, integrity, continuity and institutional reputation. The library is yet to take a strategic approach to RDM issues and there is clearly a dearth in RDM expertise within the library system. The study recommends that the library must be proactive in advocating and promoting RDM issues at UG, but first, the Librarians must take advantage of numerous existing opportunities to build their capacity.
{"title":"Research Data Management (RDM) at the University of Ghana (UG)","authors":"B. K. Avuglah","doi":"10.2218/ijdc.v15i1.670","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.670","url":null,"abstract":"This article explores Research Data Management (RDM) at the University of Ghana (UG). It emphasises on institutional awareness and attitudes, and whether the University Library is officially supporting this emerging strategic interest in research focused Higher Education Institutions (HEIs). Purposive sampling was used to select information-rich respondents from across the University (i.e. Librarians, Research Administrators, ICT Managers and Senior Researchers) who were interviewed on a range of issues about RDM. Institutional documents were also reviewed to corroborate the primary data and get a deeper understanding of the research problem. The study shows that while RDM is recognised at the institutional level as good research practice and integrity issue, the concept is tenuously understood in the local community. Unsurprisingly, however, there was a general appreciation and awareness of the need for RDM and the implications for such critical concerns as security, integrity, continuity and institutional reputation. The library is yet to take a strategic approach to RDM issues and there is clearly a dearth in RDM expertise within the library system. The study recommends that the library must be proactive in advocating and promoting RDM issues at UG, but first, the Librarians must take advantage of numerous existing opportunities to build their capacity. \u0000 ","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83661640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.
{"title":"Updating the DCC Curation Lifecycle Model","authors":"Sayeed Choudhury, Caihong Huang, C. Palmer","doi":"10.2218/ijdc.v15i1.721","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.721","url":null,"abstract":"The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84083220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Prevention and Early Intervention Research Initiative is an archiving project to preserve the data and reports that were generated by twelve years of philanthropic and state investment into prevention and early intervention approaches in the children and youth sector in Ireland and Northern Ireland. The investment resulted in an extensive collection of evaluation data and reports, which collectively provide an evidence base for continued investment into PEI programmes that are shown to be effective. In 2016, the Prevention and Early Intervention Research Initiative (PEI-RI) was established to preserve the outputs from these evaluations in the national data archives, as a publicly available evidence base. The political and social significance of this collection is manifest in the range of stakeholder groups that the project is engaging with, including the community and not-for-profit organisations that operated the PEI programmes, the research teams from academic institutions that evaluated these programmes, and representatives from government departments that co-funded many of these programmes with Atlantic. This paper tells the story of the PEI-RI archiving project, describing the steps we’ve taken since 2016 to preserve and promote the PEI data. During the course of the project we realised that it would not be enough to provide access to the data alone, as "[g]enerating and collating the evidence is of no use if it never reaches the commissioners and professionals who need it" (What Works Network, 2014, pp. 6). In the second phase of our project we are creating a range of resources for practitioner and decision maker audiences which provide a pathway to the data using the archival infrastructure. The project provides a case study of curating a digital collection that is intended for multiple stakeholders with different expectations of the archived material. The PEI-RI data curator is located in the middle of a triad of data creators, data consumers and data archives, and is tasked with balancing the interests, expectations and limitations of each.
{"title":"Data Curator in the Middle: Curating Data for a Diverse Community of Stakeholders","authors":"R. Geraghty","doi":"10.2218/ijdc.v15i1.706","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.706","url":null,"abstract":"\u0000The Prevention and Early Intervention Research Initiative is an archiving project to preserve the data and reports that were generated by twelve years of philanthropic and state investment into prevention and early intervention approaches in the children and youth sector in Ireland and Northern Ireland. The investment resulted in an extensive collection of evaluation data and reports, which collectively provide an evidence base for continued investment into PEI programmes that are shown to be effective. In 2016, the Prevention and Early Intervention Research Initiative (PEI-RI) was established to preserve the outputs from these evaluations in the national data archives, as a publicly available evidence base. The political and social significance of this collection is manifest in the range of stakeholder groups that the project is engaging with, including the community and not-for-profit organisations that operated the PEI programmes, the research teams from academic institutions that evaluated these programmes, and representatives from government departments that co-funded many of these programmes with Atlantic. \u0000This paper tells the story of the PEI-RI archiving project, describing the steps we’ve taken since 2016 to preserve and promote the PEI data. During the course of the project we realised that it would not be enough to provide access to the data alone, as \"[g]enerating and collating the evidence is of no use if it never reaches the commissioners and professionals who need it\" (What Works Network, 2014, pp. 6). In the second phase of our project we are creating a range of resources for practitioner and decision maker audiences which provide a pathway to the data using the archival infrastructure. \u0000The project provides a case study of curating a digital collection that is intended for multiple stakeholders with different expectations of the archived material. The PEI-RI data curator is located in the middle of a triad of data creators, data consumers and data archives, and is tasked with balancing the interests, expectations and limitations of each. \u0000","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88401456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As part of the European Commission funded FREYA project The British Library wanted to explore the possibility of developing provenance information in datasets derived from the British Library’s collections, the data.bl.uk collection. Provenance information is defined in this context as ‘information relating to the origin, source and curation of the datasets’. Provenance information is also identified within the FAIR principles as an important aspect of being able to reuse and understand research datasets. According to the FAIR principles, the aim is to understand how to cite and acknowledge the dataset as well as understanding how the dataset was created and has been processed. There is also reference to the importance of this metadata being machine readable. By enhancing the metadata of these datasets with additional persistent identifiers and metadata a fuller picture of the datasets and their content could be understood. This also adds to the veracity and understanding the dataset by end users of data.bl.uk.
{"title":"Building the Picture Behind a Dataset","authors":"Frances Madden, J. Ashton, J. Cope","doi":"10.2218/ijdc.v15i1.702","DOIUrl":"https://doi.org/10.2218/ijdc.v15i1.702","url":null,"abstract":"As part of the European Commission funded FREYA project The British Library wanted to explore the possibility of developing provenance information in datasets derived from the British Library’s collections, the data.bl.uk collection. Provenance information is defined in this context as ‘information relating to the origin, source and curation of the datasets’. Provenance information is also identified within the FAIR principles as an important aspect of being able to reuse and understand research datasets. According to the FAIR principles, the aim is to understand how to cite and acknowledge the dataset as well as understanding how the dataset was created and has been processed. There is also reference to the importance of this metadata being machine readable. By enhancing the metadata of these datasets with additional persistent identifiers and metadata a fuller picture of the datasets and their content could be understood. This also adds to the veracity and understanding the dataset by end users of data.bl.uk.","PeriodicalId":87279,"journal":{"name":"International journal of digital curation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73049350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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