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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]

Purpose: This study investigates the multifaceted barriers and facilitators affecting research data sharing across the research data lifecycle. It aims to broaden the understanding of data sharing beyond the publication phase, emphasizing the continuous nature of data sharing from generation to reuse. Design/methodology/approach: Employing a mixed-methods approach, the study integrates the Theory of Planned Behavior, the Technology Acceptance Model, and the Institutional Theory to hypothesize the influence of various factors on data sharing behaviors across the lifecycle. A questionnaire survey and structural equation modeling are utilized to empirically test these hypotheses. Findings: This study identifies critical factors influencing data sharing at different lifecycle stages, including perceived behavioral control, perceived effort, journal and funding agency pressures, subjective norms, perceived risks, resource availability, and perceived benefits. The findings highlight the complex interplay of these factors and their varying impacts at different stages of data sharing. Research limitations/implications: This study illuminates the dynamics of research data sharing, offering insights while recognizing its scope might not capture all disciplinary and cultural nuances. It highlights pathways for stakeholders to bolster data sharing, suggesting a collaborative push towards open science, reflecting on how strategic interventions can bridge existing gaps in practice. Practical implications: This study offers actionable recommendations for policymakers, journals, and institutions to foster a more conducive environment for data sharing, emphasizing the need for support mechanisms at various lifecycle stages. Originality/value: This study contributes to the literature by offering a comprehensive model of the research data lifecycle, providing empirical evidence on the factors influencing data sharing across this continuum. [ABSTRACT FROM AUTHOR]

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work. [ABSTRACT FROM AUTHOR]

The value of open research data (ORD), a key feature of open science, lies in their reuse. However, the mere online availability of ORD does not guarantee their reuse by other researchers. Specifically, previous meta-scientific research has indicated that the underutilization of ORD is related to barriers at the level of the ORD themselves, potential reusers of ORD, and the broader academic ecosystem. At the same time, sharing large datasets in an understandable and transparent format that motivates researchers to explore these datasets remains a fundamental challenge. With the present work, we propose interactive data apps (IDAs) as innovative ORD supplements that provide a means to lower barriers of ORD reuse. We demonstrate the use of two open-source Python libraries (Dash, Gradio) for IDA development using two psychological research use cases. The first use case pertains to an experimental quantitative dataset acquired in a clinical psychology setting. The second use case concerns the familiarization with data analysis workflows that are characteristic of natural language processing (NLP). For both use cases, we provide easy-to-adapt Python code that can form the basis for IDA development in similar scenarios.

In the framework of the CUNE-IIIF-ORM project the aim is to train an Artificial Intelligence Optical Character Recognition (AI-OCR) model that can automatically locate and identify cuneiform signs on photorealistic representations of Old Babylonian texts (c. 2000–1600 B.C.E.). In order to train the model, c. 200 documentary clay tablets have been selected. They are manually annotated by specialist cuneiformists on a set of 12 still raster images generated from interactive Multi-Light Reflectance images. This image set includes visualisations with varying light angles and simplifications based on the dept information on the impressed signs in the surface. In the Cuneur Cuneiform Annotator, a Gitlab-based web application, the identified cuneiform signs are annotated with polygons and enriched with metadata. This methodology builds a qualitative annotated training corpus of approximately 20,000 cropped signs (i.e. 240,000 visualizations), all with their unicode codepoint and conventional sign name. It will act as a multi-layerd core dataset for the further development and fine-tuning of a machine learning OCR training model for the Old Babylonian cuneiform script. This paper discusses how the physical nature of handwritten inscribed Old Babylonian documentary clay tablets challenges the annotation and metadating task, and how these have been addressed within the CUNE-IIIF-ORM project to achieve an effective training corpus to support the training of a machine learning OCR model. Applied computing → Document management and text processing → Document capture → Optical character recognition; Applied computing → Arts and humanities → Language translation. [ABSTRACT FROM AUTHOR]

Metadata and retrieval functions play a vital role in aiding researchers in the discovery and reuse of open data. However, the diversity of metadata elements and retrieval functions poses a challenge to data searchers' limited attentional resources. This study aims to examine the allocation of attention to metadata elements and retrieval functions and its implications for perceived value and intentions to discover and reuse open data by drawing upon the attentional drift-diffusion model, flow theory, and perceived value literature. An experiment with 48 participants was conducted to explore the proposed relationships. Multiple linear regression analysis was performed to analyze the data. The results suggest that researchers' attention to high-value functions amplifies the perceived value and motivates data discovery intention. Attention to high-value metadata elements motivates data discovery and reuse intention. In contrast, attention to low-value metadata elements hampers the perceived value and inhibits data discovery and reuse intention. These findings put forward a new lens for exploring the attention mechanisms underlying perceived value, data discovery and reuse intention and highlight the important role of the value of metadata and retrieval functions in attention mechanisms. Additionally, this paper identifies the positive effect of perceived ease of use on users' intentions to find, evaluate, and access open data. Perceived usefulness positively affects users' intentions to evaluate open data. However, in contrast to perceived intentions to reuse open data assessed by self-reported measures, perceived value is not a salient motivator of open data reuse intention measured by behavioral indicators. These findings reveal the distinct effects of perceived value on perceived intention and intentional action in data reuse. With these insights, this study develops practical strategies to optimize the design of metadata and retrieval functions in data retrieval systems. [ABSTRACT FROM AUTHOR]

The value of open research data (ORD), a key feature of open science, lies in their reuse. However, the mere online availability of ORD does not guarantee their reuse by other researchers. Specifically, previous meta-scientific research has indicated that the underutilization of ORD is related to barriers at the level of the ORD themselves, potential reusers of ORD, and the broader academic ecosystem. At the same time, sharing large datasets in an understandable and transparent format that motivates researchers to explore these datasets remains a fundamental challenge. With the present work, we propose interactive data apps (IDAs) as innovative ORD supplements that provide a means to lower barriers of ORD reuse. We demonstrate the use of two open-source Python libraries (Dash, Gradio) for IDA development using two psychological research use cases. The first use case pertains to an experimental quantitative dataset acquired in a clinical psychology setting. The second use case concerns the familiarization with data analysis workflows that are characteristic of natural language processing (NLP). For both use cases, we provide easy-to-adapt Python code that can form the basis for IDA development in similar scenarios. [ABSTRACT FROM AUTHOR]

Introduction The benefits of sharing participant-level data, including clinical or epidemiological data, genomic data, high-dimensional imaging data, or human-derived samples, from biomedical studies have been widely touted and may be taken for granted. As investments in data sharing and reuse efforts continue to grow, understanding the cost and positive and negative effects of data sharing for research participants, the general public, individual researchers, research and development, clinical practice, and public health is of growing importance. In this scoping review, we will identify and summarize existing evidence on the positive and negative impacts and costs of data sharing and how they are measured. Methods and analysis Eligible studies will report on qualitative or quantitative approaches for measuring the cost of data sharing or its impact on participant privacy, individual or public health, researcher’s careers, clinical or public health practice, or research or development. The systematic search strategy uses MeSH and text terms and is tailored for Ovid Medline, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. We will apply the Arskey and O’Malley scoping review methodology. We selected a scoping rather than a systematic review approach to address multiple related questions and provide guidance related to an emerging field. Two reviewers will conduct the title-abstract and full-text screening and data charting independently. Discrepancies will be resolved through consensus and results will be summarized in a narrative form. Conclusion Research participants, investigators, regulatory groups, ethics review committees, data protection officers, and funders cannot make informed decisions or policies about data reuse without appropriate means of measuring the effects, positive or negative, and cost of data sharing.

Abstract Background The COVID-19 pandemic has had a significant impact on mental health, with evidence suggesting an enduring mental health crisis. Studies worldwide observed increased usage of antidepressants, anxiolytics, and hypnotics during the pandemic, notably among young people and women. However, few studies tracked consumption post-2021. Our study aimed to fill this gap by investigating whether the surge in the number psychotropic drug consumers in France persisted 2 years after the first lockdown, particularly focusing on age and gender differences. Methods We conducted a national retrospective observational study based on the French national insurance database. We retrieved all prescriptions of anxiolytics, hypnotics, and antidepressants dispensed in pharmacies in France for the period 2015–2022. We performed interrupted time series analyses based on Poisson models for five age classes (12–18; 19–25; 26–50; 51–75; 76 and more) to assess the trend before lockdown, the gap induced and the change in trend after. Results In the overall population, the number of consumers remained constant for antidepressants while it decreased for anxiolytics and hypnotics. Despite this global trend, a long-term increase was observed in the 12–18 and 19–25 groups for the three drug classes. Moreover, for these age classes, the increases were more pronounced for women than men, except for hypnotics where the trends were similar. Conclusions The number of people using antidepressants continues to increase more than 2 years after the first lockdown, showing a prolonged effect on mental health. This effect is particularly striking among adolescents and young adults confirming the devastating long-term impact of the pandemic on their mental health.

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]

Seasonality patterns are reported for various psychiatric disorders. Concerning adolescents, there is an increased frequency of general emergency department visits for mental health disorders observed between March and May, as well as in October and November. We conducted a retrospective cohort study using the French health insurance medico-administrative database. We extracted psychotropic drug deliveries occurring between 2015 and 2019 for patients aged between 12 and 18 years old. Each drug delivery was classified as occurring during a school period (Sc), the summer holidays (SumH) or other shorter holidays periods (ShH). We compared the number of distinct patients, as well as the proportion of new consumers, according to week status. Anxiolytics and hypnotics were more frequently dispensed during the school periods and short breaks than during the summer holidays. Conversely, antidepressants were more commonly dispensed during the short breaks rather than school periods and summer holidays. The stressful effects induced by schooling appear to be addressed in the first line by anxiolytics and hypnotics, while antidepressants are more frequently introduced during school holidays. [ABSTRACT FROM AUTHOR]

Adverse drug reaction are defined as "harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product". In France, adverse effects due to medicines are reported to the French National Agency of Medicines (ANSM) by the healthcare professionals or consumers. The objective of this study was to implement a tool that facilitates the utilization of ANSM reports by synthesizing information to effectively inform prescribers and users. We focused on 3 psychotropic classes: antidepressants, antipsychotics and anxiolytics. We extracted relevant data from the ANSM website through a webscraping process, based on the names of molecules in these 3 classes: antidepressants, antipsychotics, and anxiolytics. We implemented a web interface with R Shiny that provides three panels: (i) a presentation of the active ingredient with the fewest reports for a selected adverse effect category, (ii) the adverse reactions for a selected active ingredient ranked in descending order, and (iii) a comparison of two active ingredients where, for each adverse effect, the active ingredient with the fewest reported adverse drug events (ADEs) is displayed. Our application allows for synthesizing information to effectively inform prescribers and users. In the ANSM existing interface, molecules can only be viewed one by one, and the ratio needs to be calculated manually, making it difficult to compare molecules. It is important to note that this is not a prescription assistance device but rather for informational purposes. In the future, the application may be expanded to include other categories of molecules. Finally, the indicators provided by our tool could be compared to those from other pharmacovigilance databases. [ABSTRACT FROM AUTHOR]

This scoping review investigates sustainability in the reuse of health data on a technological, intra-organizational, inter-organizational, and regulatory level. Thereby, it focuses on the evolutionary, relational, and durational perspective of sustainability. The study highlights various challenges in achieving data sustainability, from regulatory norms such as FAIR principles towards data governance processes and responsibilities in organizations that facilitate data sharing. By highlighting the need for economic sustainability of health data sharing platforms and adapted principles for data sharing, this study aims to analyze current practices that aim for sustainability in the secondary use of health data. [ABSTRACT FROM AUTHOR]

Intensive care units (ICUs) provide care for critical patients at high risk of morbidity and mortality, and require continuous monitoring of clinical, biological and, imaging parameters. Collaborative ventures have enabled the emergence of large open access databases for the secondary use of Electronic Health Records (EHRs). The objective of this work was to evaluate the availability of scripts and datasets in publications based on ICU open-access databases. We included 910 original articles based on four ICU open-access databases (Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, and Medical Information Mart for Intensive Care). The majority of the studies did not provide their data management scripts (n=839, 92.9%), neither the analysis script (n=843, 93.4%) in the article. Attempts to contact the 845 corresponding authors in question resulted in 89.11% (n=753) of our e-mail requests going unanswered over a two-month period. We received 51 automated messages (55.43%) indicating that emails have not been delivered, while 6 messages (6.52%) redirected to alternative email addresses. Only 20 corresponding authors (18.18%) answered, finally providing the requested materials. Despite scientific journals recommendations to share materials, our study unveils the absence of crucial components for the replication of studies by other research teams. [ABSTRACT FROM AUTHOR]

Background: While open datasets are adopting FAIR principles to improve their discovery and use, restricted data—those only accessible via request or application—have fallen behind. Metadata is not an inherent characteristic of restricted data, which limits its ability to be found and used. To better understand discoverability and accessibility of restricted data, this study reviewed restricted health data sources to determine how they describe their datasets and access procedures, what descriptive commonalities exist across data sources, and to what extent the commonalities we found can be accommodated within existing metadata schemas. Methods: This study extracted dataset and access information provided by a sample of 48 restricted data sources, identified commonalities across these data sources to develop possible metadata elements for restricted data, and mapped these metadata elements to existing metadata schemas (e.g., DataCite) to evaluate how well they accommodate information supplied by restricted data sources. Results: Restricted data sources describe their datasets (35 commonalities) and access procedures (27 commonalities) in similar ways. Dataset descriptions aligned with existing metadata schemas, with the DDI-Lifecycle and -Codebook schemas receiving 91.4% and 85.7% exact matches respectively with the dataset elements we identified. Access procedures did not align with metadata available in existing schemas. Discussion: While descriptive dataset metadata for restricted data sources will make their data more findable, the accessibility of these datasets could be significantly improved by structured metadata capturing data access information. Presently, metadata schemas do not accommodate the level of detail restricted data sources provide about access procedures and requirements.

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

In the past years, there has been an increased interest in sharing and reusing research data. While the importance of sharing data is urgent for enabling collaboration, many research projects are currently struggling with setting up a strategy and the right infrastructure for enabling such data-driven collaboration among the project’s researchers. Through an analysis of the Cluster of Excellence project Internet of Production as a use case, we have found that to enable researchers to share and find research data, a suitable platform is needed, as well as processes that smoothly blend into existing research data management practices. We argue that leveraging data management plans from a medium of documentation to a dynamic knowledge source enhances overview and discoverability of data, while integrating easily into day-to-day workflows of researchers. We present a tool, the Dataset Finder, which is built on the basis of data management plans, and allows users to intuitively query available datasets. The current functionalities of the tools are discussed, results of a preliminary evaluation, as well as potential future features.

Introduction: This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods: This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results: The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources. Conclusions: A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data. [ABSTRACT FROM AUTHOR]

Background: NYU Langone Health offers a collaborative research block for PGY3 Primary Care residents that employs a secondary data analysis methodology. As discussions of data reuse and secondary data analysis have grown in the data library literature, we sought to understand what attitudes internal medicine residents at a large urban academic medical center had around secondary data analysis. This case report describes a novel survey on resident attitudes around data sharing. Methods: We surveyed internal medicine residents in three tracks: Primary Care (PC), Categorical, and Clinician- Investigator (CI) tracks as part of a larger pilot study on implementation of a research block. All three tracks are in our institution's internal medicine program. In discussions with residency directors and the chief resident, the term "secondary data analysis" was chosen over "data reuse" due to this being more familiar to clinicians, but examples were given to define the concept. Results: We surveyed a population of 162 residents, and 67 residents responded, representing a 41.36% response rate. Strong majorities of residents exhibited positive views of secondary data analysis. Moreover, in our sample, those with exposure to secondary data analysis research opined that secondary data analysis takes less time and is less difficult to conduct compared to the other residents without curricular exposure to secondary analysis. Discussion: The survey reflects that residents believe secondary data analysis is worthwhile and this highlights opportunities for data librarians. As current residents matriculate into professional roles as clinicians, educators, and researchers, libraries have an opportunity to bolster support for data curation and education. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have been widely applied in the field of computer vision. In convolutional neural networks, convolution operations account for more than 90% of the total computational workload. The current mainstream approach to achieving high energy-efficient convolution operations is through dedicated hardware accelerators. Convolution operations involve a significant amount of weights and input feature data. Due to limited on-chip cache space in accelerators, there is a significant amount of off-chip DRAM memory access involved in the computation process. The latency of DRAM access is 20 times higher than that of SRAM, and the energy consumption of DRAM access is 100 times higher than that of multiply–accumulate (MAC) units. It is evident that the "memory wall" and "power wall" issues in neural network computation remain challenging. This paper presents the design of a hardware accelerator for convolutional neural networks. It employs a dataflow optimization strategy based on on-chip data reordering. This strategy improves on-chip data utilization and reduces the frequency of data exchanges between on-chip cache and off-chip DRAM. The experimental results indicate that compared to the accelerator without this strategy, it can reduce data exchange frequency by up to 82.9%. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed. [ABSTRACT FROM AUTHOR]

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers. [ABSTRACT FROM AUTHOR]

Purpose: Much of the research in data discovery is centered on the users’ viewpoint, frequently overlooking the perspective of those who develop and maintain the discovery infrastructure. Our goal is to conduct a comparative study on research data discovery, examining both support specialists’ and researchers’ views by merging new analysis with prior research insights. Methods: This work summarizes the studies the authors have conducted over the last seven years investigating the data discovery practices of support specialists from different disciplines. Although support specialists were not the main target of some of these studies, data about their perspectives was collected. Our corpus comprises in-depth interviews with 6 social science support specialists, interviews with 19 researchers and 3 support specialists from multiple disciplines, a global survey with 1630 researchers and 47 support specialists, and a use case analysis of 25 support specialists. In the analysis section, we juxtapose the fresh insights on support specialists’ views with the already documented perspectives of researchers for a holistic understanding. The latter is primarily discussed in the literature review, with references made in the analysis section to draw comparisons. Results: We found that support specialists’ views on data discovery are not entirely different from those of the researchers. There are, however, some differences that we have identified, most notably the interconnection of data discovery with general web search, literature search, and social networks. Conclusion: We conclude by proposing recommendations for different types of support work to better support researchers’ data discovery practices.

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.

Introduction: Historic data, often in analog format, is a valuable resource for assessing effects of directional changes in climate and climatic variability. However, historic data can be difficult to locate, interpret, and reformat into a useful state.Methods: Teams of scientists, librarians, archivists, and data managers at four US institutions have undertaken various projects to gather, describe, and in some cases, transform historic data. They have also surveyed researchers who either possess historic data or have used it in their work.Results: Historic data projects involved locating data, writing data descriptions, and connecting with individuals who had knowledge about the data’s collection. The surveys and interviews found that researchers valued historic data and were worried that it was at risk of loss. They noted the lack of best practices.Discussion: Each project attempting to rescue or enhance access to historic data has a unique path but being guided by FAIR principles should be at the core whether or not the end result is machine-readable data. Working with a team incorporating librarians, archivists, and data managers can aid individual researchers’ in producing accessible, and reusable datasets. There is much work to be done in raising awareness about the value of historic data but motivating factors for doing so include its usefulness in environmental research and other disciplines and its risk of loss as researchers retire and are unsure of how to save historic data, both in analog and electronic formats.

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers.

Objective: To investigate maternal and neonatal outcomes after a delivery in France in 2019, according to hospital characteristics and the impact of distance and time of travel on mother and newborn. Methods: All parturients above 18 years of age who delivered in 2019 and were identified in the French health insurance database were included, with their newborns, in this retrospective cohort study. Main outcome measures were Severe Maternal Morbidity score and the Neonatal Adverse Outcome Indicator (NAOI). Results: Among the 733 052 pregnancies included, 10 829 presented a severe maternal morbidity (1.48%) and 77 237 had a neonatal adverse outcome (10.4%). Factors associated with an unfavorable maternal or neonatal outcome were Obstetric Comorbidity Index, primiparity, and cesarean or instrumental delivery. Prematurity was associated with less severe maternal morbidity but more neonatal adverse outcomes. Time of travel above 30 min was associated with a higher NAOI rate. Conclusions: Results suggest the efficiency of regionalization of perinatal care in France, although a difference in both outcomes persists according to unit volume, suggesting the need for a further step in concentrating perinatal care. Perinatal care organization should focus on mapping the territory with high‐level, high‐volume maternity throughout the territory; this suggests closing down high‐volume units and improving low‐volume ones to maintain coherent mapping. Synopsis: High‐volume facilities improve maternal and neonatal outcome after delivery. Distance from hospital is associated with a worse neonatal outcome. [ABSTRACT FROM AUTHOR]

This article discusses the importance of reusing existing data in research. In addition to reuse data for replication of earlier findings and for answering extended or new research questions, we propose a third application of data reuse: studying the phenomenon from an alternative causal perspective. We focus on the reuse of data with a necessity causal perspective ("if not X, then not Y") as employed in necessary condition analysis (NCA). Such reuse of data offers additional insights compared with those obtained from the conventional probabilistic causal perspective ("if X, then probably Y") as employed in regression analysis. NCA is gaining recognition in various fields, including strategic management. Reusing data for conducting NCA is an efficient way to get new causal insights. We provide recommendations on how to use NCA with existing data and emphasize the importance of transparency when reusing data. [ABSTRACT FROM AUTHOR]

What are the motivations and sought gratifications leading information science researchers to share and to reuse research data? Research data are both datasets and supplementary materials such as interview guides or questionnaires. The theoretical backgrounds of this study are the Lasswell Formula of Communication, the Uses and Gratifications Theory, and the Self-determination Theory, which formed the basis for the construction of an interview guide and the interpretation of the interview transcripts. We performed 11 in-depth interviews with German information scientists, all with experiences with data. The results demonstrate that research data sharing is not a rare practice among information scientists. Due to problems with different information horizons of the sharing and the reusing researchers, the reusing of data sets is much rarer than the reuse of supplementary materials. [ABSTRACT FROM AUTHOR]

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With the emergence and development of the digital economy, digital society, and digital government, data culture has also emerged. Library data culture includes five aspects, namely library culture of data-related skills and attitudes, library culture of data sharing, library culture of data use and reuse, li- brary data ethics and governance culture, and library-specific data culture. Taking representative academic libraries at home and abroad as examples, the path of library data culture construction is analyzed from the perspectives of strategic planning and best practices, including data-driven research, data-driven services, data-driven management, and data-driven decision-making. The construction of library data culture faces risks such as data security and intellectual property, as well as challenges in digital technology development and application, capacity building and talent cultivation, and external competition. Libraries can sustainably carry out data culture construction by establishing a data mindset, strengthening capacity building, enhancing talent cultivation, prudently addressing process risks, and actively embracing external challenges. [ABSTRACT FROM AUTHOR]

Abstract Background Hip arthroplasty is a frequently performed procedure in orthopedic surgery, carried out in almost all health structures for two main issues: fracture and coxarthrosis. Even if volume–outcome relationship appeared associated in many surgeries recently, data provided are not sufficient to set surgical thresholds neither than closing down low-volumes centers. Question With this study, we wanted to identify surgical, health care-related and territorial factors influencing patient’ mortality and readmission after a HA for a femoral fracture in 2018 in France. Patients and methods Data were anonymously collected from French nationwide administrative databases. All patients who underwent a hip arthroplasty for a femoral fracture through 2018 were included. Patient outcome was 90-day mortality and 90-day readmission rate after surgery. Results Of the 36,252 patients that underwent a HA for fracture in France in 2018, 0.7% died within 90-day year and 1.2% were readmitted. Male and Charlson comorbidity index were associated with a higher 90-day mortality and readmission rate in multivariate analysis. High volume was associated with a lower mortality rate. Neither time of travel nor distance upon health facility were associated with mortality nor with readmission rate in the analysis. Conclusion Even if volume appears to be associated with lower mortality rate even for longer distance and time of travel, the persistence of exogenous factors not documented in the French databases suggests that regionalization of hip arthroplasty should be organized with caution. Clinical relevance As volume–outcome relationship must be interpreted with caution, policy makers should not regionalize such surgery without further investigation.

[Purpose/Significance] In 2021, the Ministry of Education of China issued the "Norms for the Construction of Digital Campuses in Higher Education Institutions (Trial)", which mentioned that universities should attempt to form a new model of academic exchange and resource sharing through the construction of institutional repositories (IRs). The construction of knowledge resources in university IRs is gradually developing towards the next generation IRs. Research data resources are an important component of the future resources. From the perspective of the generation, acquisition, and existence of information resources in universities, the institutional resources in universities primarily consist of resources generated by "industry, academia, research and application" activities, including teaching resources, scientific research resources, design resources, scientific activity resources, etc., which are specifically reflected in the activity process such as scholar resources, research activity resources, research process resources, and research achievements resources. Research data resources are studied as the research object in this article. [Method/Process] This paper analyzes the formation mechanism of institutional knowledge resources, and conduct research on the integration, management and reuse of research data resources, including: 1) internal factors of research data, data generation, source, type, structure, and dependency mode; 2) research on external elements of data, such as data standards, unique identifiers, data registration, data protocols, data rights, data reuse, and data sharing Through the study of internal and external factors, the influencing factors, functions and roles, responsibilities and rights, permissions and attribution of data integration management and data reuse were sorted and interpreted and feasible methods were designed for research data management, collection, implementation, and registration. In order to ensure the effectiveness of data management, a series of standards and schemes have been developed, such as data type and format standards, metadata schemes, and data guardianship demand survey templates. The purpose is to achieve data discovery, interoperability, and reuse through continuous monitoring of scientific data. The basic rules of reuse are mainly divided into: 1) reuse: the concept of reuse, reuse, sharing, and incomplete equivalence in reuse; 2) sharing: possible to be used; 3) protocol usage: discussion about how to use it; and 4) rights use: complying with the data copyright agreement. [Results/Conclusions] Through research courses, the development and implementation of favorable data management and reuse strategies have clarified the data objects, management set management services, and data reuse permissions of research data in university IRs. We have clarified the current situation and urgent issues to integrate research data into the long-term preservation, management, and sharing and reuse system of knowledge resources in university IRs. We have defined the responsibilities, management mechanisms, standardized business processes, permission attributes, data exchange platforms, data registration, and data reuse of research data subjects by category, region, and field, and provided some suggestions and guarantee measures for the establishment of a data management center in China.