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The data collected in the clinical registries or by data reuse require some modifications in order to suit the research needs. Several common operations are frequently applied to select relevant patients across the cohort, combine data from multiple sources, add new variables if needed and create unique tables depending on the research purpose. We carried out a qualitative survey by conducting semi-structured interviews with 7 experts in data reuse and proposed a standard workflow for health data management. We implemented a R tutorial based on a synthetic data set using Jupyter Notebook for a better understanding of the data management workflow. more...

Due to the development of China's high-speed railway, a large amount of field monitoring test data has been accumulated from previous scientific research and engineering practice. However, due to the backwardness of data management technology at that time, the test data were mostly "shelved" at the end of the project, and the scientific research value was not fully realized. Owing to the continuous progress of computer technology, the deformation monitoring system has gradually become a prevalent research topic. However, most of the current system research focuses more on the implementation of automatic monitoring technology and visualization model presentation, ignoring the design of the underlying database. Aiming at the aforementioned problems and taking Kunming-Nanning High-speed Railway roadbed field monitoring test as an example, we analyze and organize relevant engineering information and test data based on the E-R conceptual model, categorize the corresponding relationship pattern, and appropriately complete the design of the historical case database table structure and optimize the data management method. After the data is normalized, it will be batch loaded to a MySQL5.7-based database management system to establish the Kunming-Nanning High-speed Railway roadbed monitoring test history case database. Thus, integrated data management and user role-based authority management will be realized. This case database, a means of developing a secure and reliable database, can conveniently and efficiently display data as per user needs. Based on the aforementioned case database, a corresponding data display system is established. The system can clearly and intuitively display the logical structure and organization of the roadbed monitoring test of Kunming-Nanning High-speed Railway, and to achieve data reuse, and enable the subsequent relevant personnel to understand the project information for in-depth analysis. [ABSTRACT FROM AUTHOR] more...

Data reuse strategy is an effective method to save storage space and improve data utilization in data management. In view of the successful application of deep learning in the field of text mining, a data reuse strategy based on deep learning is proposed for high dimensional data's pattern and instance similarity. With traditional feature analysis and deep learning model of convolutional neural network, the pattern similarity of data dimension is analyzed so as to optimize the similar dimension pairs among high dimensional data sets. Combining inner-attention mechanism, a semantic similarity model IA-LSTM is designed for instance similarity, which can build the association mapping among data entities by the calculation of the similarity of short text. Based on the pattern and instance similarity in the proposed strategy, reusable data entities are discovered, and column storage is designed to improve data reuse efficiency. [ABSTRACT FROM AUTHOR] more...

Archaeologists frequently use written guidelines such as site manuals, recording forms, and digital prompts during excavations to create usable data within and across projects. Most written guidelines emphasize creating either standardized datasets or narrative summaries; however, previous research has demonstrated that the resulting datasets are often difficult to (re)use. Our study analyzed observations and interviews conducted with four archaeological excavation teams, as well as interviews with archaeological data reusers, to evaluate how archaeologists use and implement written guidelines. These excavation team and reuser experiences suggest that archaeologists need more specific best practices to create and implement written guidelines that improve the quality and usability of archaeological data. We present recommendations to improve written guidelines that focus on a project's methods, end-of-season documentation, and naming practices. We also present a Written Guidelines Checklist to help project directors improve their written guidelines before, during, and after fieldwork as part of a collaborative process. Ideally, these best practices for written guidelines will make it easier for team members and future reusers to incorporate their own and others' archaeological data into their research. [ABSTRACT FROM AUTHOR] more...

This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands. In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a "carrot" rather than as a "stick." Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature. Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts. Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university. We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers. [ABSTRACT FROM AUTHOR] more...

Most archaeological investigations in the United States and other countries must comply with preservation laws, especially if they are on government property or supported by government funding. Academic and cultural resource management (CRM) studies have explored various social, temporal, and environmental contexts and produce an ever-increasing volume of archaeological data. More and more data are born digital, and many legacy data are digitized. There is a building effort to synthesize and integrate data at a massive scale and create new data standards and management systems. Taxpayer dollars often fund archaeological studies that are intended, in spirit, to promote historic preservation and provide public benefits. However, the resulting data are difficult to access and interoperationalize, and they are rarely collected and managed with their long-term security, accessibility, and ethical reuse in mind. Momentum is building toward open data and open science as well as Indigenous data sovereignty and governance. The field of archaeology is reaching a critical point where consideration of diverse constituencies, concerns, and requirements is needed to plan data collection and management approaches moving forward. This theme issue focuses on challenges and opportunities in archaeological data collection and management in academic and CRM contexts. [ABSTRACT FROM AUTHOR] more...

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom‐up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise. [ABSTRACT FROM AUTHOR] more...

Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing. more...

Purpose: The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach: This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings: This study identifies earth and environmental scientists' information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications: While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications: The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications: By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value: While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record. [ABSTRACT FROM AUTHOR] more...

Purpose: The purpose of this study is to explore current practices, challenges and technological needs of different data repositories. Design/methodology/approach: An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey. Findings: In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%). Originality/value: This study identifies the current challenges and needs for improving data repository functionalities and user experiences. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204 [ABSTRACT FROM AUTHOR] more...

Investments in research that produce scientific and scholarly data can be leveraged by enabling the resulting research data products and services to be used by broader communities and for new purposes, extending reuse beyond the initial users and purposes for which the data were originally collected. Submitting research data to a data repository offers opportunities for the data to be used in the future, providing ways for new benefits to be realized from data reuse. Improvements to data repositories that facilitate new uses of data increase the potential for data reuse and for gains in the value of open data products and services that are associated with such reuse. Assessing and certifying the capabilities and services offered by data repositories provides opportunities for improving the repositories and for realizing the value to be attained from new uses of data. The evolution of data repository certification instruments is described and discussed in terms of the implications for the curation and continuing use of research data. more...

Older data in paper or analog format (e.g., field/lab notebooks, photos, maps) held in labs, offices, and archives across research institutions are an often overlooked resource for potential reuse in new scientific studies. However, there are few mechanisms to help researchers find existing analog data in order to reuse it. Yet, in the literature, reuse of historical data is particularly important in studies of biodiversity and climate change. We surveyed life science researchers at the University of Minnesota to understand and explore current and potential future use of historical data, attitudes around sharing and reusing data, and preservation of the data. Large amounts of historical data existed on our campus. Most researchers had reused or shared it, and many continued to add to their data sets. Some data had been scanned, over half of researchers have re-keyed some of their data into machine-readable format, and nearly all that were converted to a digital format were stored on unstable platforms and legacy formats. Researchers also expressed concerns about long-term preservation plans, or who to contact for assistance in planning for the future of the data, since much of these data are at risk for loss. Currently produced digital data sets are subject to guidelines and requirements developed at a national level. Solutions for historical analog data could benefit from a similar high-level treatment, and it will take experts from various fields to lead this effort. Given libraries’ expertise in data management and preservation, librarians are in a position to collaborate on devising cross-disciplinary solutions. more...

Objective: The objective of this paper is to illustrate the importance and complexities of working with historical analog data that exists on university campuses. Using a case study of fruit breeding data, we highlight issues and opportunities for librarians to help preserve and increase access to potentially valuable data sets. Methods: We worked in conjunction with researchers to inventory, describe, and increase access to a large, 100-year-old data set of analog fruit breeding data. This involved creating a spreadsheet to capture metadata about each data set, identifying data sets at risk for loss, and digitizing select items for deposit in our institutional repository. Results/Discussion: We illustrate that large amounts of data exist within biological and agricultural sciences departments and labs, and how past practices of data collection, record keeping, storage, and management have hindered data reuse. We demonstrate that librarians have a role in collaborating with researchers and providing direction in how to preserve analog data and make it available for reuse. This work may provide guidance for other science librarians pursing similar projects. Conclusions: This case study demonstrates how science librarians can build or strengthen their role in managing and providing access to analog data by combining their data management skills with researchers’ needs to recover and reuse data. The substance of this article is based upon a panel presentation at RDAP Summit 2019. more...

Research data management practices have gained momentum the world over. This is due to increased demands by governments and other funding agencies to have research data archived and shared as widely as possible. This paper sought to establish the data sharing practices of researchers in South Africa. The study further sought to establish the level of collaboration among researchers in sharing research data at the university level. The outcomes of the survey will help the researchers to develop appropriate data literacy awareness programmes meant to stimulate growth in data sharing practices for the benefit of research, not only in South Africa, but the world at large. A survey research method was used to gather data from willing public universities in South Africa. A similar study was conducted in other countries such as the United Kingdom, France and Turkey but the Researchers believe that circumstances in the developed world may differ with the South African research environment, hence the current study. The major finding of this study was that most researchers preferred to use data produced by others but less keen on sharing their own data. This study is the first of its kind in South Africa which investigates data sharing practices of researchers from multi-disciplinary fields at the university level and will contribute immensely to the growing body of literature in the area of research data management. more...

With the advent of the Web, increased emphasis on "research data management," and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities. [ABSTRACT FROM AUTHOR] more...

Research data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP. [ABSTRACT FROM AUTHOR] more...

In the data economy, many organisations, particularly SMEs may not be in a position to generate large amounts of data themselves, but may benefit from reusing data previously collected by others. Organisations that collect large amounts of data themselves may also benefit from reusing such data for other purposes than originally envisioned. However, under the current EU personal data protection legal framework, constituted by the General Data Protection Regulation, there are clear limits and restrictions to the reuse of personal data. Data can only be reused for purposes that are compatible with the original purposes for which the data were collected and processed. This is at odds with the reality of the data economy, in which there is a considerable need for data reuse. To address this issue, in this article we present the concept of a Data Reuse Impact Assessment (DRIA), which can be considered as an extension to existing Privacy and Data Protection Impact Assessments (PIAs and DPIAs). By adding new elements to these existing tools that specifically focus on the reuse of data and aspects regarding data ethics, a DRIA may typically be helpful to strike a better balance between the protection of personal data that is being reused and the need for data reuse in the data economy. Using a DRIA may contribute to increased trust among data subjects that their personal data is adequately protected. Data subjects, in turn, may then be willing to share more data, which on the long term may also be beneficial for the data economy. [ABSTRACT FROM AUTHOR] more...

Biodiversity informatics is the application of informatics techniques to ecology and biodiversity sciences. The premise is utilizing natural history collections/data, such as specimens and biodiversity observations, with information and communication technology. During the past two decades, biodiversity informatics has improved dramatically and has been applied increasingly in ecology science. In this paper, I review biodiversity informatics for ecology with a particular focus on the treatment of data. First, I discuss the traditional perspective of data collection and its usage in the ecological community. Then, I provide an overview of the trajectory of changing perspectives of data treatment relative to developing biodiversity informatics (i.e., the infiltration of the data reuse and sharing concepts). Specifically, I discuss the significance of data reuse, which offers numerous benefits for research, and data sharing with their supporting mechanisms and case studies. Finally, I discuss potential future developments in biodiversity informatics relative to the field of ecology. [ABSTRACT FROM AUTHOR] more...

Data sharing and reuse are regarded as important components of the research workflow and key elements in open science. While reuse is well-documented in some circumstances, the utility of data sharing for all domains is less clear, and limited evidence of wide-spread demand can make it challenging to justify effort and funds required to format, document, share, and preserve data. This paper describes a project that: (1) surveyed authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines to determine if data were generated for their article and their knowledge of reuse by other researchers, and (2) surveyed authors who cited these 2015 articles to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications. While the results revealed reuse in many situations (and deemed important in these cases), the survey results and researcher supplied comments also indicated that data does not play the same role in all studies or even in studies that build on previous ones. [ABSTRACT FROM AUTHOR] more...

Data are one of the primary outputs of science. Although certain subdisciplines of biology have pioneered efforts to ensure their long-term preservation and facilitate collaborations, data continue to disappear, owing mostly to technological, regulatory, and ideological hurdles. In this article, we describe the important steps toward proper data management and archiving and provide a critical discussion on the importance of long-term data conservation. We then illustrate the rise in data archiving through the Joint Data Archiving Policy and the Dryad Digital Repository. In particular, we discuss data integration and how the limited availability of large-scale data sets can hinder new discoveries. Finally, we propose solutions to increase the rate of data preservation, for example by generating mechanisms insuring proper data management and archiving, by providing training in data management, and by transforming the traditional role of research institutions and libraries as data generators toward managers and archivers. [ABSTRACT FROM AUTHOR] more...

The last few years have seen considerable progress in terms of integrating individual elements of the research ecosystem with the so-called FAIR Principles (Wilkinson et al. 2016), a set of guidelines to make research-related resources more findable, accessible, interoperable and reusable (FAIR). This integration process has lots of technical as well as social components and ramifications, some of which resulted in dedicated terms like that of a FAIR Digital Object (FDO) which stands for research objects (e.g. datasets, software, specimens, publications) having at least a minimum level of compliance with the FAIR Principles. As the volume, breadth and depth of FAIR data and the variety of FAIR Digital Objects as well as their use and reuse continue to grow, there is ample opportunity for multidimensional interactions between generators, managers, curators, users and reusers of data, and the scope of data quality issues is diversifying accordingly. This poster looks at two ways in which individual collections of FAIR Digital Objects interact with the wider FAIR research landscape. First, it considers communities that curate, generate or use data, metadata or other resources pertaining to individual collections of FAIR Digital Objects. Specifically, which of these community activities are affected by higher or lower compliance of a collection's FDOs with the FAIR Principles? Second, we will consider the case of communities that overlap across FAIR collections - i.e. when some community members are engaged with several collections, possibly through multiple platforms - and what this means in terms of challenges and opportunities for enhancing findability, accessibility, interoperability and reusability between and across FAIR silos. [ABSTRACT FROM AUTHOR] more...

Data-intensive science presents new opportunities as well as challenges to research libraries. The cyberinfrastructural challenge, although chiefly technological, also involves social-economic and human factors, therefore requires a deep understanding of what roles research libraries should play in the research lifecycle. This paper discusses the rationale and motivations behind a research project to investigate effective library big data cyberinfrastructure strategies. [ABSTRACT FROM AUTHOR] more...

Objectives: (1) to identify common errors in data organization and metadata completeness that would preclude a “reader” from being able to interpret and re-use the data for a new purpose; and (2) to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used.Methods: We used directed qualitative content analysis to assess and categorize data and metadata errors identified by peer reviewers of data papers published in the Ecological Society of America’s (ESA) Ecological Archives. Descriptive statistics provided the relative frequency of the errors identified during the peer review process.Results: There were seven overarching error categories: Collection & Organization, Assure, Description, Preserve, Discover, Integrate, and Analyze/Visualize. These categories represent errors researchers regularly make at each stage of the Data Life Cycle. Collection & Organization and Description errors were some of the most common errors, both of which occurred in over 90% of the papers.Conclusions: Publishing data for sharing and reuse is error prone, and each stage of the Data Life Cycle presents opportunities for mistakes. The most common errors occurred when the researcher did not provide adequate metadata to enable others to interpret and potentially re-use the data. Fortunately, there are ways to minimize these mistakes through carefully recording all details about study context, data collection, QA/ QC, and analytical procedures from the beginning of a research project and then including this descriptive information in the metadata. more...

Introduction: We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can't be shared, requiring a distributed network approach where data processing is performed prior to data sharing. Case Descriptions and Variation Among Sites: We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge(EU-ADR),Observational Medical Outcomes Partnership(OMOP), the Food and Drug Administration's (FDA's) Mini-Sentinel, and the Italian network--the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE). Findings: National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++. Conclusion: Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases. an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings. [ABSTRACT FROM AUTHOR] more...

In the age of "big data," scientific researchers are increasingly struggling with how to manage, organize, and make sense of the vast amount of data that often characterizes scientific research in the 21st century. With their expertise in knowledge management, information professionals can be valuable collaborators for research teams facing these challenges. This article discusses just a few of the myriad opportunities for librarians and other information professionals to become involved with research teams and provide valuable support for the management, analysis, and preservation of research data. [ABSTRACT FROM AUTHOR] more...

Sharing research data has the potential to make research more reproducible and efficient. Scientific research is a complex process and it is crucial that at the different stages of this process, researchers handle data in a way that will allow sharing and reuse. In this paper, we present a framework for the different steps involved in managing research data: a hierarchy of research data needs, and describe some of our own ongoing efforts to support these needs. Creating a good data ecosystem that supports each of these data needs requires collaboration between all parties that are involved in the generation, storage, retrieval and use of data: researchers, librarians, institutions, government offices, funders, and also publishers. We are actively collaborating with many other participants in the research data field, to develop a data ecosystem that enables data to be more useful, and reusable, throughout science and the humanities. [ABSTRACT FROM AUTHOR] more...

Scientific data management is performed to ensure that data are curated in a manner that supports their qualified reuse. Curation usually involves actions that must be performed by those who capture or generate data and by a facility with the capability to sustainably archive and publish data beyond an individual project's lifecycle. The Australian Antarctic Data Centre is such a facility. How this centre is approaching the administration of Antarctic science data is described in the following paper and serves to demonstrate key facets necessary for undertaking polar data management in an increasingly connected global data environment. [ABSTRACT FROM AUTHOR] more...

The collection, organization, and long-term preservation of resources are the raison d'être of archives and archivists. The archival community, however, has largely neglected science data, assuming they were outside the bounds of their professional concerns. Scientists, on the other hand, increasingly recognize that they lack the skills and expertise needed to meet the demands being placed on them with regard to data curation and are seeking the help of 'data archivists' and 'data curators.' This represents a significant opportunity for archivists and archival scholars but one that can only be realized if they better understand the scientific context. [ABSTRACT FROM AUTHOR] more...

• A declarative approach for FAIR data management in environmental sciences - A data acquisition framework for semantic interoperability and unit transformation. • Logical reasoning infers compatibility between semantically heterogeneous datasets. • A case study to automatically transform meteorological files of four agricultural models. The data deluge following the rise of Internet of Things contributes towards the creation of non-reusable data silos. Especially in the environmental sciences domain, syntactic and semantic heterogeneity hinders data re-usability as most times manual labour and domain expertise is required. Both the different syntaxes under which environmental timeseries are formatted and the implicit semantics which are used to describe them contribute to this end. Usually, the real meaning of data is obscured in a combination of short data labels, titles and various value codes, that require domain or institutional knowledge to decipher. The FAIR data principles for scientific data sharing are stewardship offer a framework based on community-adopted metadata. In this work, we present the Environmental Data Acquisition Module (EDAM) which focuses on data interoperability and reuse, and deals with syntactic and semantic heterogeneity using a template approach. Data curators draft templates to describe in an abstract fashion the syntax of the timeseries datasets they want to acquire or disseminate. They complement each template with a metadata file, which is used to annotate observables and their properties (including physical quantities and units of measurement) with terms from an ontology. EDAM employs a reasoner to infer compatibility among syntactically and semantically heterogeneous datasets, and enables timeseries, format and units of measurement transformation on-the-fly. Our approach utilizes a local ontology to store metadata about datasets, which enables EDAM to acquire and transform datasets which were originally stored with different semantics and syntaxes. We demonstrate EDAM in a case study where we transform meteorological input files of four agricultural models. Our approach, allows to cut across environmental data silos and facilitate timeseries reusability, as it enables users to (a) discover datasets in other formats, (b) transform them and (c) reuse them in their scientific workflows. This directly contributes to the toolshed for FAIR data management in environmental sciences. EDAM implementation has been released under an open-source license. [ABSTRACT FROM AUTHOR] more...