Resources for GDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the GDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting GDR metadata for federation or inclusion in their local catalogs.

The aim of the Data Rescue & Curation Best Practices Guide is to provide an accessible and hands-on approach to handling data rescue and digital curation of at-risk data for use in secondary research. We provide a set of examples and workflows for addressing common challenges with social science survey data that can be applied to other social and behavioural research data. The goal of this guide and set of workflows presented is to improve librarians’ and data curators’ skills in providing access to high-quality, well-documented, and reusable research data. The aspects of data curation that are addressed throughout this guide are adopted from long-standing data library and archiving practices, including: documenting data using standard metadata, file and data organization; using open and software-agnostic formats; and curating research data for reuse.

Background: With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. Methods: The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. Findings: Most respondents displayed what we describe as high and moderate risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.

ABSTRACT The exponential increase of published data and the diversity of systems require the adoption of good practices to achieve quality indexes that enable discovery, access, and reuse. To identify good practices, an integrative review was used, as well as procedures from the ProKnow-C methodology. After applying the ProKnow-C procedures to the documents retrieved from the Web of Science, Scopus and Library, Information Science & Technology Abstracts databases, an analysis of 31 items was performed. This analysis allowed observing that in the last 20 years the guidelines for publishing open government data had a great impact on the Linked Data model implementation in several domains and currently the FAIR principles and the Data on the Web Best Practices are the most highlighted in the literature. These guidelines presents orientations in relation to various aspects for the publication of data in order to contribute to the optimization of quality, independent of the context in which they are applied. The CARE and FACT principles, on the other hand, although they were not formulated with the same objective as FAIR and the Best Practices, represent great challenges for information and technology scientists regarding ethics, responsibility, confidentiality, impartiality, security, and transparency of data.

Best Practices (description updated 2/6/2023)

MIA - Best Practices

Ethical Data Management

This dataset contains the primary data for the project "Effectiveness of data auditing as a tool to reinforce good Research Data Management (RDM) practice". It includes surveys of PIs and researchers and use of a central data repository (SDS) by each laboratory.

1. Centers for Disease Control and Prevention (CDC). Best Practices for Comprehensive Tobacco Control Programs. Funding. CDC's Best Practices for Comprehensive Tobacco Control Programs is an evidence-based guide to help states plan and establish effective tobacco control programs to prevent and reduce tobacco use. Data are reported at total and per capita funding levels. Data include recommended, upper, and lower total funding levels for state programs, in addition to funding breakdowns by intervention areas such as: State and Community Interventions, Health Communication Interventions, Cessation Interventions, Surveillance and Evaluation, and Administration and Management.

Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

The Observatory of Religious Pluralism in Spain is an initiative of the Ministry of Justice, the Spanish Federation of Municipalities and Provinces (FEMP) and the Pluralism and Coexistence Foundation. This is a tool for the visibility and dissemination of good practices of public management of religious diversity. Makes it possible to consult by subject and type of institution (City; Educational centres; Health Centres, Services and Establishments; Penitentiary institutions; Armed forces; Public Police Services).

Resources for OEDI data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of OEDI. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting OEDI metadata for federation or inclusion in their local catalogs.

Customer service best practices - MIA

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

Methods To investigate the data-related practices of psychology researchers, we adapted a survey developed as part of our previous study of neuroimaging researchers. The survey was distributed via Qualtrics (http://www.qualtrics.com) from January 25 to March 25, 2019. Before beginning the survey, participants were required to verify that they were at least 18 years old and gave their informed consent to participate. Participants who did not meet these inclusion criteria or who did not complete at least the first section of the survey were not included in the final data analysis. After filtering, 274 psychology researchers from 31 countries participated in our survey.

All code for data collection and visualization is included in the Jupyter notebooks included here.

Instrument Data Management Market Outlook

According to our latest research, the global Instrument Data Management market size reached approximately USD 2.9 billion in 2024, driven by the surging demand for efficient data handling and compliance management across laboratory and industrial environments. The market is expected to exhibit a robust CAGR of 9.1% during the forecast period, reaching an estimated USD 6.2 billion by 2033. This significant growth trajectory is primarily attributed to the increased digitization of laboratory workflows, the rising adoption of advanced analytical instruments, and the growing emphasis on regulatory compliance in highly regulated industries such as pharmaceuticals, biotechnology, and food & beverage.

One of the key growth factors driving the Instrument Data Management market is the exponential increase in data generated by modern laboratory instruments. With the proliferation of high-throughput technologies and automated systems, laboratories are producing vast volumes of complex data that require efficient management, storage, and analysis. Traditional manual data handling methods are inadequate for ensuring data integrity, traceability, and compliance with stringent regulatory standards. As a result, organizations are increasingly turning to instrument data management solutions that offer centralized data repositories, automated data capture, and seamless integration with existing laboratory information management systems (LIMS). The need to streamline laboratory operations and enhance data-driven decision-making is further accelerating market adoption.

Another critical factor contributing to market expansion is the evolving regulatory landscape across various sectors. Industries such as pharmaceuticals, biotechnology, and food & beverage are subject to rigorous regulatory requirements, including Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP), and FDA 21 CFR Part 11. These regulations mandate robust data management practices to ensure data accuracy, security, and auditability. Instrument data management platforms enable organizations to maintain comprehensive audit trails, enforce user access controls, and automate compliance workflows, thereby mitigating the risk of non-compliance and associated penalties. The increasing focus on data integrity and regulatory adherence is compelling organizations to invest in advanced data management solutions.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) capabilities are also shaping the future of the Instrument Data Management market. Modern solutions are leveraging AI-driven analytics to extract actionable insights from instrument-generated data, enabling predictive maintenance, process optimization, and enhanced quality control. The adoption of cloud-based deployment models further facilitates remote access to data, collaboration across geographically dispersed teams, and scalable storage solutions. These innovations are not only enhancing operational efficiency but also providing organizations with a competitive edge in their respective industries. As digital transformation accelerates, the demand for sophisticated instrument data management platforms is expected to rise steadily.

From a regional perspective, North America continues to dominate the Instrument Data Management market, accounting for the largest market share in 2024. The region's leadership is attributed to the presence of a well-established pharmaceutical and biotechnology industry, high R&D investments, and stringent regulatory frameworks. Europe follows closely, driven by robust healthcare infrastructure and increasing adoption of laboratory automation technologies. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by expanding healthcare and life sciences sectors, growing investments in research and development, and increasing awareness about data integrity and compliance. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by rising industrialization and regulatory modernization initiatives.

Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

Resources for MHKDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the MHKDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting MHKDR metadata for federation or inclusion in their local catalogs.

The NC OneMap Geoportal has matured into an essential resource many professionals rely upon to perform their daily business. Data managers and custodians are urged to adopt the recommendations contained in this document. Doing so will ensure that NC OneMap continues to be an easy-to-use and reliable data discovery tool providing resources that have consistent capabilities and are well-documented. This document provides guidance for data managers to configure geospatial resources in uniform, consistent ways for successful discovery within NC OneMap.

Origin

This poster is for SciDataCon 2023 poster exhibition

Description

Nowadays research teams everywhere face challenges in the better management of research data for cross-domain collaboration and long-term use. The research teams are often diverse in their composition as in terms of application domains, computational resources, research methods, and lab practices, just to name a few. To overcome these differences, we believe that it is essential to foster a culture of sharing experiences and ideas about research data management planning among and within the teams. By doing so, we can navigate around common barriers as well as grow data expertise together.

In this poster, we report on a joint effort between a research data repository (the depositar; https://data.depositar.io/) and a biodiversity information facility (TaiBIF; https://portal.taibif.tw/) in engaging with local research communities in fostering good data management practices. The depositar is a data repository open to researchers worldwide for the deposit, discovery, and reuse of datasets. TaiBIF (Taiwan Biodiversity Information Facility) builds essential information infrastructures and promotes the openness and integration of biodiversity data. Both teams are based in Academia Sinica, Taiwan. TaiBIF has been organizing workshops in Taiwan for the management, mobilization, application, and integration of biodiversity information. In the past years, the depositar team has been taking part in TaiBIF workshops to organize hand-on courses on writing Data Management Plans (DMPs). These workshops offer training and guidance to help researchers acquire practical skills in research data management. The course activities are designed to encourage workshop participants not only to draft DMPs but also to engage in the peer review of their draft DMPs. As a result, we empower the workshop participants to take ownership of their data management practices and contribute to the overall improvement of their data management skills.

Our templates for drafting and reviewing DMPs are derived from Science Europe's Practical Guide to the International Alignment of Research Data Management (extended edition). We have created online instructional materials where participants can simulate the process of writing DMPs based on their own research projects. Furthermore, we facilitate peer review activities in small groups by means of the DMP evaluation criteria listed in the Science Europe's guide. The entire process is conducted through open sharing, allowing participants to learn from each other and to share data management practices within their knowledge domains. Subsequently, we select outstanding DMPs from these exercises which serve as examples and discussion points for future workshops. This approach allows us to increase the availability of data management solutions that are closely aligned with specific domains. It also fosters a friendly environment that encourages researchers to organize, share, and improve upon their data management planning skills.

Reference

Science Europe. (2021). Practical Guide to the International Alignment of Research Data Management - Extended Edition. (W. W. Tu & C. H. Wang & C. J. Lee & T. R. Chuang & M. S. Ho, Trans.). https://pid.depositar.io/ark:37281/k516v4d6w