Find research datasets worth reusing

opencc-by-4.0Oct 2020View details →

Datasets of Twitter mentions and publications in Information Science & Library Science and Microbiology

Datasets used in the study 'Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics'. Microbiology publications (mic_publiccations.tsv). Dataset of 101,206 Microbiology publications with their author keywords. Microbiology mentions (mic_mentions.tsv). Dataset of 328,110 Twitter mentions to Microbiology publications. Information Science & Library Science publications (lis_publications.tsv). Dataset of 8452 Information Science & Library Science publications with their author keywords. Information Science & Library Science mentions (lis_mentions.tsv). Dataset of 35,411 Twitter mentions to Information Science & Library Science publications.

opencc-by-4.0Jan 2021View details →

Dataset - research methodology annotation (Information Science)

Datasets used for developing text mining methods for extracting research methods reported in Information Science journal articles.

opencc-by-4.0Nov 2021View details →

Information literacy in the area of Library and Information Science. A bibliometric analysis in Latin America, from the Lens database (2001-2020).

The results of scientific production on ALFIN (2001-2020) in the areas of Library and Information Science are shown. All BIC journals were identified from Latindex. Then it was verified whether these journals were contained in the following databases: Web of Science (Core Collection and Scielo Citation Index), Scopus, Lens and Dimensions. The Lens database was chosen for retrieving records on ALFIN and performing the bibliometric analysis, as it has the highest coverage of BIC journals in Latindex. The trend and growth of scientific production were evaluated according to authors and year of publication; the productivity of authors was analyzed using Lotka's Law and the dispersion of the literature according to Bradford's Law. The degree, index and coefficient of collaboration were determined and collaboration networks were identified according to authors. The results show that scientific production on ALFIN in Latin America, reached a peak between 2017 and 2018, presenting a decrease from 2019 onwards. It was also observed that the production, collaboration between authors and the number of journals is predominantly Brazilian.

opencc-by-4.0Feb 2024View details →

Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"

Reproducibility data for the manuscript "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability", it contains data and script for : <ol> <li> The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020) </li> <li> The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness </li> <li> The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2. </li> </ol> <ul> <li> Column names are highlighted in italics. </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code> <h5>B. Conservation_status.txt</h5> A <code>.txt</code> file of the conservation status for France (Statut_FR) and Europe (Statut_EU) for the studied species retrieved from (UICN France et al., 2016). Only Statut_FR is used for the table S6.1. <h5>C. FBBS_trends_20122023.txt</h5> A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023. <ul> <li> Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023 </li> <li> Hab_specialization, determined from Julliard et al. 2006 approach </li> <li> infPrec, supPerc, estimate, se, pval : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance </li> </ul> <h5>D. PrepData_HDS.RData</h5> A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains : <ul> <li> ATLAS12 : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations) </li> <li> ConcordTBL : A concordance table for species names (English, French and scientific notation) </li> <li> EPOC_ODF : observation dataset, each line corresponds to detected individuals <ul> <li> UUID, Ref, ID_liste, ID, ID_place, Grid_10x10 : Columns used to identify observations, lists, sites, locations, 10x10 grids </li> <li> ID_species_Biolovision, Nom_espece, english_name, scientific_name : Species ID and names </li> <li> Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance : Lists and Observation related effort covariates and metadata </li> <li> X_Lambert93_m, Y_Lambert93_m : Observation locations in <code>(crs = 2154)</code> </li> <li> GPS_loc_observer : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations </li> <li> X_barycentre_L93, Y_barycentre_L93 : Observers location in <code>(crs = 2154)</code> </li> <li> Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more : Distance sampling related informations </li> <li> Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA : Observation metadata, used in case of a priori filter over male detection. </li> </ul> </li> <li> grid_pred_envvar : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France </li> <li> grid_pred.sf : corresponding sf object </li> <li> L93_10x10 : sf object corresponding to 10x10 grid used in 2012 atlas </li> <li> ObsVar_EPOCODF : dataframe specifying lists effort covariates </li> <li> OCCU_EPOC_ODF : Environmental covariate agregated over lists </li> <li> OCCU_EPOC_ODF_sites_envvar : Environmental covariate agregated over sites </li> <li> table.pheno : species table specifying related phenology filter </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Predictions were restrained over location known as breeding in 2012 for the sake of comparison. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> NB_data_calib : Number of observations (distance data, not sites) used for calibration </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>) </li> <li> EcartDTF_filtrage_maleOnly : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %) </li> <li> Max_dist_breaks : Maximal distance for detection function, after right-side truncation of 5% </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> MED_MEAN_Prob_Detect (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise </li> <li> MED_MEAN_Density : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise </li> <li> Significant_phi/lambda : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1) </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript. <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over metropolitan France. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates <ul> <li> debut_jour : starting day of the month for phenology filter </li> <li> debut_mois : starting month for phenology filter </li> <li> fin_jour : ending day of the month for phenology filter </li> <li> fin_mois : ending month for phenology filter </li> <li> Estim_low/up_Atlas2012 : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs) </li> <li> gregarious : logical (0,1) specifying if the species is considered gregarious during its breeding season </li> </ul> <h5>I. sessionInfo_script_XX</h5> User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script. <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript. <h5>B. <code>02_pglmm_figures.R</code></h5> Script for the calibration of the PGLMM and generation of figure 5 of the manuscript. <h5>C. <code>03_results_tables.R</code></h5> Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2) <h5>D. <code>HDS_functions.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions: <ul> <li> <code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop. <ul> <li> Species with non convergent models are skipped sending a notification to the user R interface. </li> <li> Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda") </li> <li> When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument </li> <li> Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas </li> </ul> </li> <li> <code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used. </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function. <h5>F. <code>misc_unmarked.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>). <h2>References</h2> Data was derived from the following sources: <ul> <li> Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a> </li> </ul> Other sources : <ul> <li> Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a> </li> <li> Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a> </li> <li> UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France. </li> </ul>

dryad40/100

Data from: Sectorial pathways to achieve net-zero and 1.5°C targets for Eu-27: Energy and emissions data to inform science-based decarbonization targets

Open the record for dataset details and reuse information.

publicMay 2025View details →

opencc-zeroApr 2020View details →

Science to inform policy: linking population dynamics to habitat for a threatened species in Canada

Abstract 1. Boreal forests provide numerous ecological services, including the ability to store large amounts of carbon, and are of significance to global biodiversity. Increases in industrial activities in boreal landscapes since the mid-20th century have added to concerns over biodiversity loss and climate change. Boreal forests are home to dwindling populations of boreal caribou (Rangifer tarandus caribou) in Canada, a species at risk that requires large, undisturbed landscapes for persistence. In 2012, the Canadian government defined critical habitat for boreal caribou by relating calf recruitment to disturbances. Some have questioned whether the recruitment-relationship can be extrapolated beyond the environmental conditions represented in the analysis. 2. We examined the effects of human disturbances and fire (alone and in combination) on variation in recruitment and adult female survival using data from 58 study areas in Canada. Top models were used in aspatial scenarios of landscape change to evaluate the efficacy of the critical habitat definition in achieving the recovery objectives for boreal caribou in two contrasting landscapes: Little Smoky, dominated by high levels of human disturbances, and SK1, dominated by fire. 3. The top recruitment model suggested the negative effect of fire was 3-4 times smaller than human disturbances. The top adult female survival model included human disturbances only. These results re-affirm that human disturbances are the primary factor contributing to boreal caribou declines. 4. Our aspatial scenarios suggested that undisturbed habitat would have to increase to ≥68% for Little Smoky to maintain a self-sustaining population of boreal caribou with some degree of certainty. In contrast, the SK1 population was self-sustaining with 40% undisturbed habitat when fire disturbance predominates, but could become vulnerable with increases in human disturbances (8–9%). 5. Policy implications: Our results suggest that the 65% undisturbed critical habitat designation may serve as a reasonable proxy for achieving boreal caribou recovery in landscapes dominated by human disturbances. However, some populations may be less or more vulnerable, as illustrated by the SK1 scenarios. Continued population monitoring will be essential to assessing the effectiveness of land management strategies developed for boreal caribou recovery, especially with climate change.

opencc-zeroJan 2023View details →

Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information

Overview of Dataset  This dataset was developed through a collaboration between Dalhousie University and the University of Montréal. This project aims to help break down the silos in which the two primary target audiences- information science researchers and academic librarians- conduct their research. The Canadian Publications in Library and Information Science dataset makes visible the work that librarians do and allows other Canadian researchers to discover the research of their colleagues.  The dataset contains 1,326 distinct authors, 850 of which were classified as practitioners and 476 as academics. It has a total of 13,775 records out of which 8,230 are authored by at least one academic and 5,740 are authored by at least one practitioner. File descriptions   Table 1. Canadian LIS authors table (authors) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the publication in the LIS database </td> </tr> <tr> <td> first_name </td> <td> First name of author </td> </tr> <tr> <td> last_name </td> <td> Last name of author </td> </tr> <tr> <td> full_name </td> <td> Full name of author </td> </tr> <tr> <td> status </td> <td> Academic (Ph.D. student, a postdoctoral fellow, or a professor (assistant, associate, full, emeritus) in an organizational unit offering an ALA accredited degree) or practitioner (librarian position in a Canadian university) </td> </tr> </tbody> </table>   Table 2. Works table (publications) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> pub_id </td> <td> Unique identifier for the publication in the LIS database </td> </tr> <tr> <td> doi </td> <td> Digital object identifiers </td> </tr> <tr> <td> openalex_work_id </td> <td> Identifier of the work in the OpenAlex database (URL format) </td> </tr> <tr> <td> isbn </td> <td> International standard book number (ISBN). </td> </tr> <tr> <td> doc_type </td> <td> Document type. Can take one of the following values: article; review; conference paper, book; edited book; book chapter. </td> </tr> <tr> <td> publication_year </td> <td> Year of publication </td> </tr> <tr> <td> title </td> <td> Title of the document </td> </tr> <tr> <td> source_name </td> <td> Title of the source (journal, conference, or book title for book chapters) </td> </tr> <tr> <td> author_list_full </td> <td> Full text listing of author names </td> </tr> <tr> <td> volume </td> <td> Volume number </td> </tr> <tr> <td> issue </td> <td> Issue number </td> </tr> <tr> <td> pages </td> <td> First and last pages separated by a hyphen. </td> </tr> <tr> <td> bk_edition </td> <td> Book edition </td> </tr> <tr> <td> bk_editor </td> <td> Name of book editor (for book chapters) </td> </tr> <tr> <td> publisher </td> <td> Publisher of the book/journal </td> </tr> <tr> <td> source_id </td> <td> Foreign key to the sources table </td> </tr> <tr> <td> url </td> <td> URL for the publication </td> </tr> </tbody> </table>   Table 3. Author publications table (authors_publications) <table> <tbody> <tr> <td> <a name="_Hlk155194541"></a>Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the author in the authors table </td> </tr> <tr> <td> pub_id </td> <td> Unique identifier for the work in the publications table </td> </tr> <tr> <td> author_position </td> <td> Position on the byline. </td> </tr> <tr> <td> role </td> <td> Role of the author on the work (author/editor) </td> </tr> </tbody> </table> Table 4. Author IDs table (authors_ids) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the author in the authors table </td> </tr> <tr> <td> source </td> <td> Source for the identifier (e.g., OpenAlex, Scopus, Google Scholar, ORCID) </td> </tr> <tr> <td> identifier </td> <td> Identifier for the author in the source database </td> </tr> </tbody> </table>   Table 5. Publication source table (sources) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> source_id </td> <td> Unique identifier for the source </td> </tr> <tr> <td> source_name </td> <td> Name of the source </td> </tr> <tr> <td> publisher </td> <td> Publisher name for the source </td> </tr> <tr> <td> issn </td> <td> ISSN for the source </td> </tr> <tr> <td> source_type </td> <td> OpenAlex source type (e.g., journal, conference) </td> </tr> </tbody> </table>   Table 6. Institutions table (institutions) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the institution </td> </tr> <tr> <td> institution_name </td> <td> Name of the Canadian academic institution </td> </tr> <tr> <td> city </td> <td> Name of the city in which the institution is primarily located </td> </tr> <tr> <td> province </td> <td> Two-letter code of the province in which the institution is located </td> </tr> </tbody> </table>   Table 7. Institution IDs table (institutions_ids) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the institution in the institutions table </td> </tr> <tr> <td> id_source </td> <td> Source database for the identifier (e.g., OpenAlex) </td> </tr> <tr> <td> identifier </td> <td> Identifier linked to the institution in the source database </td> </tr> </tbody> </table>   Table 8. Authorship institutional affiliation table (authors_publications_institutions) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Author component of the authorship information in the authors_publications table </td> </tr> <tr> <td> pub_id </td> <td> Publication component of the authorship information in the authors_publications table </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the affiliated institution in the institutions table </td> </tr> </tbody> </table>   Table 9. Citations table (citations) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> citing_pub_id </td> <td> Unique identifier for the citing work in the publications table </td> </tr> <tr> <td> cited_pub_id </td> <td> Unique identifier for the cited work in the publications table </td> </tr> </tbody> </table>   To submit updates For those interested in submitting updates to this dataset, you may send them by email to Philippe Mongeon (PMongeon@dal.ca). Please specify whether you want to modify, add, or delete existing data entries. Files in any format (e.g., XLS, BIB, Word, or a list of DOIs) are accepted. Data paper Find the corresponding data paper that describes the objectives of this dataset and the steps of its creation here: <a href="https://arxiv.org/abs/6053305">https://arxiv.org/abs/6053305</a>. How to cite this dataset Sauvé, J.-S., Hare, M., Krause, G., Poitras, C., Riddle, P., & Mongeon, P. (2024). Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14302591" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14302591</a>

opencc-by-4.0Dec 2021View details →

Information Science & Library Science Landmarks

This dataset was used in a study for the Information Science & Library Science Landmarks.

opencc-by-4.0Aug 2022View details →

Description of the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft & JITA Classification System of Library and Information Science

The two excel files contain data about the collection of the <a href="https://pages.cms.hu-berlin.de/ibi/BHR/">Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft</a> (date of scraping data: March 20, 2021) and the collection of the <a href="http://eprints.rclis.org/view/subjects/"> JITA Classification System of Library and Information Science</a> (date of scraping data: March 20, 2021).

opencc-by-4.0Jun 2024View details →

Survey of library information science graduates' readiness to create an information system in the library (using open science as an example)

Survey "Readiness of library information science graduates to create an information system in the library (on the example of open science)" The research is aimed at identifying the degree of readiness of future specialists in the field of library and information science to work in new and relevant areas of library activities related to the development of information infrastructure of open science. The survey consists of 4 thematic blocks and contains 21 questions. The survey was attended by 54 students from 10 Russian universities, studying in the direction of training 51.03.06 "Library and Information Science". The results of this survey will serve as a basis for the development of professional development programmes on various aspects of the functioning of open science for specialists in the field of library and information activity. Опрос «Готовность выпускников направления библиотечная информационная наука к созданию системы информирования в библиотеке (на примере открытой науки)» Исследование направленно на выявление степени готовности будущих специалистов в области библиотечно-информационной деятельности к работе в новых и актуальных направлениях деятельности библиотек, связанных с развитием информационной инфраструктуры открытой науки. Опрос состоит из 4-х тематических блоков и содержит 21 вопрос. В опросе приняли участие 54 студента из 10 российских вузов, обучающихся по направлению подготовки 51.03.06 «Библиотечно-информационная деятельность». Результаты данного опроса послужат основой разработки программ повышения квалификации по различным аспектам функционирования открытой науки для специалистов в области библиотечно-информационной деятельности.

opencc-by-4.0Jan 2019View details →

[DATA_SCIENCE] Interviews: The Secure Anonymised Information Linkage Databank (SAIL)

This is a set of interview transcripts executed by Niccolò Tempini between September 2015 and October 2016, as part of the ERC project "The Epistemology of Data-Intensive Science", and in the context of a case study of SAIL. Please read the "Notes on transcript editing" document for further information. Two papers that specifically make use of these interviews have been published as of 2018: <ul> <li>Tempini, N., Leonelli, S., 2018. Concealment and discovery: The role of information security in biomedical data re-use. Soc Stud Sci 48, 663–690. <a href="https://doi.org/10.1177/0306312718804875">https://doi.org/10.1177/0306312718804875</a></li> <li>Tempini, N., 2016. Science Through the “Golden Security Triangle”: Information Security and Data Journeys in Data-intensive Biomedicine, in: Proceedings of the 37th International Conference on Information Systems (ICIS 2016). <a href="https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/">https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/20/</a></li> </ul> The transcripts document SAIL researchers' experience of infrastructure development and data curation and re-use practices. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter. You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu.

opencc-by-4.0Aug 2021View details →

Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".

This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper: Ponti, M., Seredko, A. <a href="http://doi.org/10.1057/s41599-022-01049-z">Human-machine-learning integration and task allocation in citizen science.</a> Humanit Soc Sci Commun 9, 48 (2022). https://doi.org/10.1057/s41599-022-01049-z The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.

opencc-zeroAug 2023View details →

Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets

This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data): 1. Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario. 2. Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed. 3. Sector folder (CSV): The data contained are the same as those described in point 2. 4. Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector. 5. Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. 6. Scope emissions folder (CSV): The data contained are the same as those described in point 5.

opencc-by-4.0Sep 2023View details →

Supplementary information to "What does ChatGPT know about natural science and engineering?"

This Excel workbook contains the survey data and data analysis from the manuscript "What does ChatGPT know about natural science and engineering?" by Schulze Balhorn et al.

publicSep 2023View details →

Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets

Open the record for dataset details and reuse information.

publicApr 2020View details →

Science to inform policy: linking population dynamics to habitat for a threatened species in Canada

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