Software tools used to collect and analyze data. Parentheses for analysis software indicate the tools participants were taught to use as part of their education in research methods and statistics. “Other” responses for data collection software were largely comprised of survey tools (e.g. Survey Monkey, LimeSurvey) and tools for building and running behavioral experiments (e.g. Gorilla, JsPsych). “Other” responses for data analysis software largely consisted of neuroimaging-related tools (e.g. SPM, AFNI).

Description of qualitative and quantitative results related to theme 2: Modulation of treatment decisions by external influences.

This is a hands-on workshop on the management of qualitative social science data, with a focus on data sharing and transparency. While the workshop addresses data management throughout the lifecycle – from data management plan to data sharing – its focus is on the particular challenges in sharing qualitative data and in making qualitative research transparent. One set of challenges concerns the ethical and legal concerns in sharing qualitative data. We will consider obtaining permissions for sharing qualitative data from human participants, strategies for (and limits of) de-identifying qualitative data, and options for restricting access to sensitive qualitative data. We will also briefly look at copyright and licensing and how they can inhibit the public sharing of qualitative data.

A second set of challenges concerns the lack of standardized guidelines for making qualitative research processes transparent. Following on some of the themes touched on in the talk, we will jointly explore some cutting edge approaches for making qualitative research transparent and discuss their potentials as well as shortcomings for different forms of research.

The statistical test data obtained from the research are related to the students' pre-test and post-test.

This survey investigates Research Data Management (RDM) practices across five Swiss higher education institutions, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on Active Data Management, particularly during research projects, including a range of topics such as:

Storage and backup solutions

Data and code sharing platforms

Documentation and metadata usage

Compliance with legal and ethical standards

Long-term data preservation strategies

Use of open formats and open-source software

Adoption of Data Management Plans (DMPs)

This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy.

Included in this dataset:

A CSV and XLSX file with the aggregated, anonymized data from the survey.

Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.

A README file providing context.

This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the numb...

An analysis of the University of Leicester Research Data Management Survey September 2015.

Drug Abuse Treatment Outcome Study - Adolescent (DATOS-A) was a multisite, prospective, community-based, longitudinal study of adolescents entering treatment. It was designed to evaluate the effectiveness of adolescent drug treatment by investigating the characteristics of the adolescent population, the structure and process of drug abuse treatment in adolescent programs, and the relationship of these factors with outcomes. Three major types or modalities of programs included in the study were chemical dependency or short-term inpatient (STI), therapeutic community or residential (RES), and outpatient drug-free (ODF). The adolescent battery of instruments included intake, intreatment, and follow-up questionnaires based largely on the DATOS adult study DRUG ABUSE TREATMENT OUTCOME STUDY (DATOS), 1991-1994: UNITED STATES instrument format, with considerable tailoring to the adolescent population. Clients entering treatment completed two comprehensive intake interviews (Intake 1 and Intake 2), approximately one week apart. This information is provided in Parts 1 and 2 of the data collection. These interviews were designed to obtain baseline data on drug use and other behaviors, such as illegal involvement, as well as information on background and demographic characteristics, education and training, mental health status, employment, income and expenditures, drug and alcohol dependence, health, religiosity and self-concept, and motivation and readiness for treatment. The one-, three-, and six-month intreatment interviews (Parts 3, 4, and 7) included items on treatment access, intreatment experience, and psychological functioning, as well as questions replicated from some of the domains in the Intake 1 and 2 questionnaires. The 12-month post-treatment follow-up interview (Part 5) included questions replicated from the previous interviews, and also included post-treatment status. Part 6 includes variables for time in treatment and interview availability indicators. The Measures Data (Part 8) were generated by using the Diagnostic and Statistical Manual of Mental Disorders (Rev. 3rd ed., DSM-III-R) (American Psychiatric Association, 1987). The variables in Part 8 give either the DSM-III-R level of dependence to a drug category or they describe whether the subject meets the DSM-III-R standard for a particular disorder. The 12-Month Follow-up Urine Result data (Part 9) provide the results from urine sample tests that were given to a sample of subjects at the time of the 12-Month Follow-up Interview. The urine test was used to ascertain the nature and extent of bias in the self-reports of the respondents. Urine specimens were tested for eight categories of drugs (amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine metabolite, methaqualone, opiates, and phencyclidine). The drugs covered in the study were alcohol, tobacco, marijuana (hashish, THC), cocaine (including crack), heroin, narcotics or opiates such as morphine, codeine, Demerol, Dilaudid, and Talwin, illegal methadone, sedatives and tranquilizers such as barbiturates and depressants, amphetamines or other stimulants such as speed or diet pills, methamphetamines, LSD, PCP, and other hallucinogens or psychedelics, and inhalants such as glue, gasoline, paint thinner, and aerosol sprays. The study also included drug of choice, frequency, and route of administration.

Research dataset and analysis for Cancer Treatment including statistics, forecasts, and market insights

Background: Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?

Objective: The aim of this paper is to analyse research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.

Methods: A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enabler...

IntroductionOptimise:MS is an observational pharmacovigilance study aimed at characterizing the safety profile of disease-modifying therapies (DMTs) for multiple sclerosis (MS) in a real world population. The study will categorize and quantify the occurrence of serious adverse events (SAEs) in a cohort of MS patients recruited from clinical sites around the UK. The study was motivated particularly by a need to establish the safety profile of newer DMTs, but will also gather data on outcomes among treatment-eligible but untreated patients and those receiving established DMTs (interferons and glatiramer acetate). It will also explore the impact of treatment switching.MethodsCausal pathway confounding between treatment selection and outcomes, together with the variety and complexity of treatment and disease patterns observed among MS patients in the real world, present statistical challenges to be addressed in the analysis plan. We developed an approach for analysis of the Optimise:MS data that will include disproportionality-based signal detection methods adapted to the longitudinal structure of the data and a longitudinal time-series analysis of a cohort of participants receiving second-generation DMT for the first time. The time-series analyses will use a number of exposure definitions in order to identify temporal patterns, carryover effects and interactions with prior treatments. Time-dependent confounding will be allowed for via inverse-probability-of-treatment weighting (IPTW). Additional analyses will examine rates and outcomes of pregnancies and explore interactions of these with treatment type and duration.ResultsTo date 14 hospitals have joined the study and over 2,000 participants have been recruited. A statistical analysis plan has been developed and is described here.ConclusionOptimise:MS is expected to be a rich source of data on the outcomes of DMTs in real-world conditions over several years of follow-up in an inclusive sample of UK MS patients. Analysis is complicated by the influence of confounding factors including complex treatment histories and a highly variable disease course, but the statistical analysis plan includes measures to mitigate the biases such factors can introduce. It will enable us to address key questions that are beyond the reach of randomized controlled trials.

Research dataset and analysis for Aeration Systems including statistics, forecasts, and market insights

This dataset resulted from conducting focus groups with scientists from five disciplines (atmospheric and earth science, chemistry, computer science, ecology, and neuroscience) about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. Our dataset includes five anonymized focus group transcripts in .pdf file format (one for each focus group with scientists from each discipline), our codebook as a spreadsheet in excel file format (.xlsx), and coded segments of our transcript text to visualize our data analysis in an excel spreadsheet in excel file format (.xlsx).

This dataset presents the assessment tool used to analyze 20 Data Management Plan (DMP) templates on the Argos platform, along with the pre-print of the manuscript for an article that is about to be published in the Journal Biblios of the University of Pittsburgh. The main objective of this study was to investigate the need to implement a DMP at Universidad Centroamericana José Simeón Cañas (UCA) to improve accessibility, discovery, and reuse of research. Using a qualitative case study methodology, we worked with 10 selected research groups to evaluate and adapt a base model for the DMP. The results indicated a significant improvement in research data management and a positive perception from users regarding the processing and organization of their data. This set includes the DMP format generated for UCA, as well as recommendations for other institutions interested in adopting similar data management practices, contributing to the continued growth of scholarly output and the ethical and..., Method: A qualitative case study methodology was employed, which included participant observation of researchers and administrative staff from various 2024 research groups, along with an analysis of documentation and LibGuides. A benchmarking process was also conducted, comparing 20 PGDI templates to extract the best structure and practices from various research institutions. Content analysis: This method was used to examine a set of 20 PGDI templates from the ARGOS initiative, a platform developed by OpenAIRE and EUDAT for planning and managing research data. A systematic review of the structure and content of each of these templates was conducted, assessing the clarity, consistency, and adequacy of the information presented. Through this content analysis, key elements were identified that needed to be incorporated or improved in the base template provided to UCA research groups. This process allowed us to highlight best practices and identify areas that required additional attention, ..., , # Data from: Data management plan (DMP): Towards more efficient scientific management at the Universidad Centroamericana José Simeón Cañas

https://doi.org/10.5061/dryad.1zcrjdg25

Description of the Data and File Structure

README for the Dataset: Implementation of a Data Management Plan (DMP)

Dataset Description

This dataset includes the evaluation instrument used to analyze 20 Data Management Plan (DMP) templates on the Argos platform. Additionally, the pre-print of the manuscript of the article that is set to be published in the Journal Biblios at the University of Pittsburgh has been attached. Furthermore, the format of the Data Management Plan generated for the Universidad Centroamericana José Simeón Cañas (UCA), developed from this research, is included.

Objective

The primary objective of this study was to investigate the need to implement a Data Management Plan (DMP) to improve the accessibility, discoverability...

We conducted an analysis to confirm our observations that only a very small percentage of public research data is hosted in the Institutional Data Repositories, while the vast majority is published in the open domain-specific and generalist data repositories.

For this analysis, we selected 11 institutions, many of which have been our evaluation partners. For each institution, we counted the number of datasets published in their Institutional Data Repository (IDR) and tracked the number of public research datasets hosted in external data repositories via the Data Monitor API. External tracking was based on the corpus of 14+ mln data records checked against the institutional SciVal ID. One institution didn’t have an IDR.

We found out that 11 out of 11 institutions had most of their public research data hosted outside of their institution

We will be happy to expand it by adding more institutions upon request.

This survey aims to investigate research data management practices in academic institutions. The survey comprises questions common to all institutions as well as institution-specific ones. Common questions were drafted in the frame of a collaboration between several RDM services: Tu Delft (team effort), EPFL (team effort), University of Cambridge (notably Marta Busse) and University of Illinois (notably Heidi Imker). The first survey was run by TU Delft and EPFL only end of 2017. In total, 659 responses where collected (423 from TU Delfg and 236 from EPFL) and are published here. The first lines of the response table contains the question asked to researchers. The second line indicates from which institution the data is coming form and wether it was anonymized. Each further line contains the response of a researcher.

More information about this survey as well as the exact survey questions and a detailed description how the survey might be re-used by other institutions is available on the project page on the Open Science Framework: htts://osf.io/mz3fx/ For any questions contact datastewards@tudelft.nl or researchdata@epfl.ch

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

The Global AI Data Management Market size was valued at around USD 23.8 billion in 2023 & is estimated to grow at a CAGR of around 24% during the forecast period 2024-30.

The supporting dataset in the result part of the thesis entitle " Investigation of efficacy and mechanisms of pro-apoptotic natural products for colon cancer treatment".The datasets are comprised of 3 folders, which are classified as supporting data in Chapter 4, Chapter 5, and Chapter 6 of the PhD thesis. It is comprised of raw data (experimental files, excel files, images), analysis data, and prism files for statistical analysis and graphs. The datasets are comprised of Excel data of the MTT tests, experimental files of the flow cytometry test, original pictures of WB with markers, and raw data from the animal studies.

This Data Management Plan describes the data management life cycle for the data, a Research Project of EXC IntCDC will collect, process and/or generate. Moreover, it describes whether and how this data is being used and/or made publicly available for verification and re-use and how the data will be curated and preserved after the end of the project.