The aim of this survey was to collect feedback about existing training programmes in research data management for researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate students across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2016. 191 responses were received.

researchcomputer/research-data dataset hosted on Hugging Face and contributed by the HF Datasets community

The NIST RDaF is a map of the research data space that uses a lifecycle approach with six high-level lifecycle stages to organize key information concerning research data management (RDM) and research data dissemination. Through a community-driven and in-depth process, stakeholders identified topics and subtopics?programmatic and operational activities, concepts, and other important factors relevant to RDM. All elements of the RDaF framework foundation?the lifecycle stages and their associated topics and subtopics?are defined. Most subtopics have several informative references, which are resources such as guidelines, standards, and policies that assist stakeholders in addressing that subtopic. Further, the NIST RDaF team identified 14 Overarching Themes which are pervasive throughout the framework. The Framework foundation enables organizations and individual researchers to use the RDaF for self-assessment of their RDM status. The RDaF includes sample ?profiles? for various job functions or roles, each containing topics and subtopics that an individual in the given role is encouraged to consider in fulfilling their RDM responsibilities. Individual researchers and organizations involved in the research data lifecycle can tailor these profiles for their specific job function using a tool available on the RDaF website. The methodologies used to generate all features of the RDaF are described in detail in the publication NIST SP 1500-8.This database version of the NIST RDaF is designed so that users can readily navigate the various lifecycle stages, topics, subtopics, and overarching themes from numerous locations. In addition, unlike the published text version, links are included for the definitions of most topics and subtopics and for informative references for most subtopics. For more information on the database, please see the FAQ page.

Abstract copyright UK Data Service and data collection copyright owner.

Scotland 1 - fieldwork was completed during the week of June 24-30, 1974.
A quota sample of 1,034 individuals were interviewed in 37 constituencies.


Main Topics:

Demographics; party preferences; party affiliation and voting intention; the Scottish National Party; attitudes to the governance of Scotland; Scottish independence; Scottish Assembly/Parliament.

An Effective Research data management (RDM) practices associated with more benefits particularly in increasing research impact, enabling the data reuse, data preservation and archive for future use. To maintain research integrity, researcher hold the responsivity to practice the effective research data management with data which they have been generate while pursuing research activities, but researcher needs the supports from either libraries or universities to establish proper data management practices. Therefore authors of aimed to conduct an assessment to understand the researcher perceptions, current practices on research data management and their willingness to share data if libraries or universities offer the some support in managing and storing research data. The study undertaken the online survey method to collect the data and it includes 34 researchers’ perceptions on research data generation, various types and formats of research data generated, infrastructure for data storage and archive, practices of research data sharing, awareness on research data repositories etc. The results provided most of researcher generate the research data irrespective of various types & formats, but they are not more aware about proper research data management and sharing though researcher have been preserving research data using offline infrastructure like desktop, pen drives, hard disks, CD/DVDs etc. and most of researcher very much keen to avail under the library support in managing research data. The study helps a better understanding of the current practices and needs of researcher at Central University of Gujarat about research data management and sharing. From the study results author provides suggestions for improve the researcher awareness on research data management by conducting training, consulting, special lectures, seminars and workshops of research data management, sharing, and storage.A survey with simple online questionnaire method was used for this study to collecting the data regarding researchers’ perceptions on research data management in the Central University of Gujarat (CUG). The authors set out to the following questions in online questionnaire:

· Have you generated any research data while you are pursuing research activities?

· Roughly what kind/ type of research data generated or created while you are pursuing research activities?

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What format and volume of research data generated?

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Where do you store your research data for future use?

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Does the practice of research data sharing exist in your discipline/school/centre/ department?

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How important is research data sharing and make available for free access?

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Are you aware of any research data repositories?

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Do you have submitted your research data to any following research data repositories?

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Do you feel that the libraries should offer some support in managing, storing and archiving your research data for future use?

Mail communication channel was used to send online questionnaire to the researchers and there were three attempts to get the responses. The study involves the Thirty Four (34) researchers’ responses from various schools and department of Central University of Gujarat. Statistical tools were used to analyse & interpret the collected data from survey method.

This archive publishes and preserves short and long-term research data collected from studies funded by:Forest Service Research and Development (FS R&D)Joint Fire Science Program (JFSP)Aldo Leopold Wilderness Research Institute (ALWRI)Of special interest, our collection includes data from a number of Forest Service Experimental Forests and Ranges.Each archived data set (i.e., "data publication") contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.

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

Description of rust files (1-8): Data 1: Chemical composition of leaves of eight alternate hosts of Cronartium spp. The aim of the study was to investigate the chemical variation of leaves of susceptible and resistant alternate hosts of Cronartium spp. It was aimed to identify chemicals that distinguish susceptible and resistant closely related species from one another. The studied plant species were Melampyrum sylvaticum, M. pratense, Veronica longifolia, V. chamaedrys, Impatiens balsamina, I. glandulifera, Ribes nigrum and R. rubrum. Twenty plants per species were investigated. The data (four accurate mass files) consisted of automatic chemical data given by the LCMS masspectrometer device. Each peak shown as peak area in relation to retention time indicated a chemical compound. The chemical data from samples were measured in separate runs in positive and negative mode. Chromatography and MS parameters were identical, while only some source settings were optimized individually for each polarity. The fifth file contained concentrations of 12 selective chemicals in the 160 leaf samples. The plant leaves were collected in the Oulu city area, air dried, cold stored and extracted in methanol in Luke's (Oulu) Lynet laboratory. Twenty plants from eight plants species each were used. The chemical analysis was done in Biocenter of the University of Oulu using a LCMS mass spectrometer. The device measured automatically from the samples peak areas representing chemical compounds. Based on this data, chemicals that separated significantly susceptible and resistant alternate host pairs from each other were identified. In addition, based on literature, concentrations of 12 selected compounds were calculated and compared between susceptible and resistant species. The used protocols were presented in detail in Piispanen et al. 2023, Eur. J. Pl. Pathol., 165, 677-682. The data contains features generated by the Compound Discoverer software as specified in Piispanen et al. 2023, Eur. J. Pl. Pathol., 165, 677-682. Each feature represents a compound characterized by accurate mass, chromatographic retention time, and the peak area. The fifth file contained results for targeted (absolute) quantification of 12 selected chemicals in the 160 leaf samples. Four Excel-data of negative and positive accurate mass data from LCMS were connected to the data. The data includes from each compound calculated automatically by the device Molecular weight, Retention time, Maximun area, and Raw areas of every peak. The data have been shown in two positive and negative accurate mass files. Based on these files, the compounds significantly differing between plant species pairs were calculated. In addition, 12 selected compounds were picked up from the data in all samples and compared between plant species. The variables in the Excel data are Sample/Plant species, Sample size mg, Extraction 1, Extraction 2, Extraction total and callculation of the amount of 12 compounds. The Plant species were: Melampyrum sylvaticum, M. pratense, Veronica chamaedrys, V. longifolia, Impatiens glandulifera, I. balsamina, Ribes nigrum and R. rubrum. The chemical compounds were chlorogenic acid, caffeic acid, syringic acid, p-coumaric acid, rutin, hyperoside, ferulic acid, quercitrin, myricetin, luteolin, apigenin and chamepherol. The results were presented in Piispanen et al. 2023, Eur. J. Pl. Pathol.,165,677-692. In Materials 1-3, the LCMS-data was collected by Biocenter, University of Oulu, where the responsible person was Dr. Ulrich Bergmann. The extractions and other calculations were done in Luke. Data 2: Temporal, spatial and leaf age variation of 12 chemicals of leaves of Melampyrum pratense and M. sylvaticum. The aim of this study was to investigate the chemical variation of 12 compounds between susceptible and resistant alternate hosts, temporal variation within growing season, spatial variation between locations and variation between young and old plant leaves. The data consisted of results from 440 leaf samples of M. pratense and M. sylvaticum collected in 1-3 locations in Oulu city either in late June, mid-July or late August. Samples represented either young or old leaves. The results represented concentrations of 12 selective compounds. The analyses were done using LCMS. Leaf samples of M. pratense and M. sylvaticum were collected from 5 locations in the Oulu city area. Samples were collected also in early, mid- and late season. Leaves were analysed also from young and old leaves of the plants. Ten plants per species were collected in each collection. The samples were analysed similar to samples of material 1 as well as also the 12 selected chemicals were using LCMS. The used protocols were presented in Piispanen et al. 2023, Eur. J. Pl. Pathol., 165, 677-692. A manuscript Piispanen et al. 2024 from the results is under process. The Excel-data consisted of the amounts of 12 compounds found in the samples. The weights were based on LCMS-analysis. The variables in the Excel.-file Material 2.xlsx were Extraction date, Collection date, Plant species (M. sylvaticum or M. pratense), No. of plants (1-10), young/old leaves (n=young leaves, v=old leaves), No. of extraction, Weight (mg), Sample number (401-840), Amounts of compounds (ng/mg): chlorogenic acid, caffeic acid, syringic acid, p-coumaric acid, rutin, hyperoside, ferulic acid, quercitrin, myricetin, luteolin, apigenin and caempherol. NF=Not found=0 in calculations. Minus values=0 in calculations. . = Missing data. A paper of the results Piispanen et al. 2024 is under process. Data 3: Effect of Cronartium spp. infection on 12 chemicals in leaves of susceptible alternate hosts of Cronartium spp. The aim of this study was to investigate the chemical changes in 12 compounds after Cronartium spp. inoculation just after infection before fruitbody formation. The data consisted of concentrations of 12 selected chemicals from inoculated and control leaves either 3 or 6 days after inoculation. The inoculations with Cronartium spp. were done in the greenhouse for three susceptible alternate hosts. The analyses of compounds were done using LCMS. All leaves of 20 susceptible plants of C. pini, P. lactiflora and I. balsamina, were inoculated with C. pini in the greenhouse by dusting spores on leaves and incubating the plants for 24 h with a moistened plastic bag. The inoculation protocol has been presented in detail in Kaitera et al. 2015, Pl. Pathol. 64, 738-747. In addition, leaves of one branch per plant of 20 R. nigrum were inoculated similarly with C. ribicola. Twenty plants were left uninoculated as controls for each species. Ten inoculated leaves were collected from all plants 3 and 6 days after inoculation. The leaves were prepared and 12 selected chemicals analysed from the samples using LCMS as in material 2. For the protocols, see Piispanen et al. 2023, Eur. J. Pl. Pathol.,165, 677-692. A manuscript Piispanen et al. 2024 from the results is under process. The Excel-data consisted of the amounts of 12 compounds found in the samples. The weights were based on LCMS-analysis. The variables in the Excel-file Material 3.xlsx were: Plant species (Ribes nigrum, Paeonia lactiflora, Impatiens balsamina), No. of plants (1-20), Inoculation (Cronartium pini, C. ribicola)/Control, Incubation days (3 or 6 days), Sample number (1-240), compounds (ng/mg): chlorogenic acid, caffeic acid, syringic acid, p-coumaric acid, rutin, hyperoside, ferulic acid, quercitrin, myricetin, luteolin, apigenin, caempherol.NF=Not found=0 in calculations. Minus values=0 in calculations. . = Missing data. A paper of the results is in process. Data 4: Temporal and spatial variation of endophytes in leaves of eight alternate hosts of Cronartium. The aim of this study was to investigate spatial variation between locations and temporal variation within growing season in endophyte composition of leaves of seven alternate hosts of Cronartium spp. The data consisted of frequencies of isolates representing 37 morphotypes of endophytes in leaves of eight alternate hosts of Cronartium spp. The morphotypes were divided among collection locations and divided into two times of collections, early and late season, which were divided in two files. Also the genetic identification of representative endophytes of different morphotypes was included. Five healthy leaves per plant from five plants of eight susceptible and resistant alternate hosts of Cronartium spp. were collected from one to three locations in Oulu city area in late June and early September. Five small pieces were cut sterily from each leaf, sterilized and inserted on agar. Endophytes emerging on the agar were transferred to pure cultures and grown at 18 C for one month after which the morphotype of the endophyte was characterised and grouped into 37 groups based on their morphology on agar. Endophytes representing the most common morphotypes and alternate hosts were identified genetically. The isolation procedure, genetic analysis and the results have been presented in Piispanen et al. 2024, Eur. J. Pl. Pathol. (under review). The Excel-data of file Morphotypes_early season.xlsx and Morphotypes_late season.xlsg contain frequencies of endophytes from eight plants species collected in late June or early September from 1-3 locations and classified in different morphotypes. The variables were Morphotype (1-37), Plant species: Melampyrun sylvaticum, M. pratense, Veronica chamaedrys, V. longifolia, Impatiens glandulifera, I. balsamina, Ribes nigrum, R. rubrum/Areas (1-3), Total. The results of the DNA-analysis are included in Excel-file DNA-analyses.xlsg containing the variables: Sample, Isolate, Plant species (Melampyrun sylvaticum, M. pratense, Veronica chamaedrys, V. longifolia, Impatiens glandulifera, I. balsamina, Ribes nigrum, R. rubrum, Morphotype, Sequence tag, Fungal species, Match (to GenBank), Sequence. Data 5: Effect of leaf extracts on germination of Cronartium spores. The aim of this study

Here, we make available the supplemental material regarding data collection from the publicaiton "Research Data Curation in Visualization : Position Paper". The dataset represents an aggregated collection of the data policies of selected publication venues in the areas of visualization, computer graphics, software, HCI, and Virtual Reality with inclusions from multimedia, collaboration, and network visualization, for the years 2021-2022. Based on a derived index, long-term preservation and data sharing are evaluated for each venue. The index ranges from No policy to Required sharing and preservation. Additionally the verbatim statements (or the lack thereof) used to reach the concluded score are also provided. Abstract: Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.

Location map of study area

Data collected from major Canadian and international research data repositories cover data storage, preservation, metadata, interchange, data file types, and other standard features used in the retention and sharing of research data. The outputs of this project primarily aim to assist in the establishment of recommended minimum requirements for a Canadian research data infrastructure. The committee also aims to further develop guidelines and criteria for the assessment and selection o f repositories for deposit of Canadian research data by researchers, data managers, librarians, archivists etc.

Dubbed recording of the making research data public panel where DH scholars and digital asset management specialists presented case studies. The panelists were: • Constance Crompton (University of Ottawa), • Karis Shearer (University of British Columbia Okanagan Campus), • Matthew Lincoln (Carnegie Mellon University), • Mikhel Proulx (Concordia University and Indigenous Digital Art Archive)

A data warehouse that integrates information on patients from multiple sources and consists of patient information from all the visits to Cincinnati Children''''s between 2003 and 2007. This information includes demographics (age, gender, race), diagnoses (ICD-9), procedures, medications and lab results. They have included extracts from Epic, DocSite, and the new Cerner laboratory system and will eventually load public data sources, data from the different divisions or research cores (such as images or genetic data), as well as the research databases from individual groups or investigators. This information is aggregated, cleaned and de-identified. Once this process is complete, it is presented to the user, who will then be able to query the data. The warehouse is best suited for tasks like cohort identification, hypothesis generation and retrospective data analysis. Automated software tools will facilitate some of these functions, while others will require more of a manual process. The initial software tools will be focused around cohort identification. They have developed a set of web-based tools that allow the user to query the warehouse after logging in. The only people able to see your data are those to whom you grant authorization. If the information can be provided to the general research community, they will add it to the warehouse. If it cannot, they will mark it so that only you (or others in your group with proper approval) can access it.

Dataset related to two harvesters and two forwarders machine working in Scots pine forest in Poland. the dataset consider a period of 18 months including machine engine parameters, STANFORD data and forest stand information. Data were acquired in the frame of CARE4C - Carbon smart forestry under climate change - H2020-MSCA-RISE-2017 Grant Agreement Number 778322.

Results of the survey sent to the managers of the libraries belonging to the Network of Spanish University Libraries (Red de Bibliotecas Universitarias Españolas - REBIUN 2014) during the first four months of 2014. The aim of the responses was to know the state of the Research Data Services (RDS).

Public opinion poll on political attitudes in Scotland

This dataset contains the original questionnaire and responses that were used to inform the presentation, Preparing UCT Libraries for Research Data Services (RDS), presented at the 18th Annual LIASA Conference on 5 October, 2017.The questionnaire was distributed to UCT Libraries' staff in September 2017. Participation was voluntary and anonymous. We received 23 responses from about 120 staff, so the data does not represent the entirety of UCT Libraries' staff attitudes, but is valuable as a starting point in analysing staff attitudes and willingness towards RDS at UCT Libraries.

Large-scale experiments conducted in the CIEM wave flume at the Universitat Politecnica de Catalunya, Barcelona, focused on hydrodynamics and sand transport under breaking waves. The SINBAD database contains datasets corresponding to two experimental campaigns: 1) a mobile-bed experiment; 2) a fixed-bed experiment. This is the mobile bed experimental dataset.

This dataset provides guidance materials and templates to help you prepare your research datasets for deposit in the U of G Research Data Repositories.Please refer to the U of G Research Data Repositories LibGuide for detailed information about the U of G Research Data Repositories including additional resources for preparing datasets for deposit. The library offers a self-deposit with curation service. The deposit workflow is as follows:Create your repository account.If you are a first-time depositor, complete the U of G Research Data Repositories New Depositor Intake Form.Activate your Data Repositories account by logging in with your U of G username and password.Once your account is created, contact us to set up your dataset creator access to your home department’s collection in the Data Repositories.Note: If you already have a Data Repositories account and dataset creator access, you can log in and begin a new deposit to your home department’s collection right away.Prepare your dataset.Assemble your dataset following the Dataset Deposit Guidelines. Use the README file template to capture data documentation.Create a draft dataset record.Log in to the Data Repositories and create a draft dataset record following the instructions in the Dataset Submission Guide.Submit your draft dataset for review.Dataset review.Data Repositories staff will review (also referred to as curate) your dataset for alignment with the Dataset Deposit Guidelines using a standard curation workflow.The curator will collaborate with you to enhance the dataset.Public release.Once ready, the dataset curator will make the dataset publicly available in the Data Repositories, with appropriate file access controls. Support: If you have any questions about preparing and depositing your dataset, please make a Publishing and Author Support Request.