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3,172 results for “public”
Job offers from Lanbide Basque Country Public Employment Portal
<p>Based on the provided sample from the "Job_offers_from_Lanbide_Basque_Country_Public_Employment_Portal" dataset, here is a summarized description in English:</p> <p>The dataset compiles various job listings, including local offers from Lanbide, wider European offers, public sector job listings, and offers from other job portals managed within the Lanbide web platform. Each record in the dataset appears to represent a unique job offer with a range of attributes detailing the position. Common attributes for each job listing include:</p> <p>- <strong>Extraction_timestamp</strong>: The date and time when the job offer was last updated, reflecting the currency of the data.<br>- <strong>Source</strong>: A numerical or categorical identifier that may correspond to the origin of the job offer, such as Lanbide, other European job portals, public offers, etc.<br>- <strong>Title</strong>: The name or title of the job position.<br>- <strong>AddressLocality</strong>: The local town or city where the job is located.<br>- <strong>AddressRegion</strong>: The broader region, province, or administrative area where the job is located.<br>- <strong>Description</strong>: A detailed description of the job, including responsibilities, requirements, and other relevant information such as contract type, shifts, start date, salary, and any specific qualifications like a disability percentage or certifications.<br>- <strong>DatePosted</strong>: The date the job was originally posted.<br>- <strong>Offer_Number</strong>: A unique identifier for the job offer.<br>- <strong>EmploymentType</strong>: The type of employment contract offered (e.g., temporary labor).<br>- <strong>Collective</strong>: Any specific community or group the job is targeting, such as people with disabilities.<br>- <strong>OccupationalCategory</strong>: The category or sector of employment, potentially aligning with standard occupational classification systems.<br>- <strong>Workday</strong>: Details regarding the work schedule.<br>- <strong>NumberPositions</strong>: The number of positions available.<br>- <strong>Participants</strong>: Possibly the number of current applicants or participants<br>- <strong>EmploymentSituation</strong>: This field could detail the employment status required.<br>- <strong>Age</strong>: Any age requirements.<br>- <strong>EducationRequirements</strong>: Educational or training requirements necessary for applicants.<br>- <strong>DriverLicense</strong>: Information on whether a driver's license is required.<br>- <strong>ExperienceRequirements</strong>: The amount of experience required.<br>- <strong>ContactInformation</strong>: Details for contacting the employer or recruiter.</p> <p>Each job listing encapsulates comprehensive information that would aid job seekers in understanding the requirements and expectations of each role and for employers or recruiters in managing the recruitment process.</p>
Experimental data associated with publication "Small-scale roughness entraps water and controls underwater adhesion"
<p>This folder contains:</p> <ol> <li>Experimental adhesion data and the JKR Fit parameters.</li> <li>Raw SFG data used to create Figure 2.</li> <li>Power spectral densities of the 4 rough diamond surfaces. </li> <li>Summary sheets containing results for dry and underwater adhesion measurements. </li> </ol>
Supplemental data for the publication: "Algal methylated compounds shorten the lag phase of Phaeobacter inhibens bacteria"
<p><strong>Data S1: </strong><em>P. inhibens </em>feature table (genes) with results from co-cultivation RNA-sequencing run. The dataset includes bacterial gene accession numbers, functional annotations, transcript abundances (TPM normalized read counts) and results of DESeq2 differential gene expression analysis. (Fig. 1, figs. S1A, S3-S4, tables S1-S2). </p> <p><strong>Data S2: </strong><em>P. inhibens </em>feature table (genes) with results from lag phase RNA-sequencing run. The dataset includes bacterial gene accession numbers, functional annotations, transcript abundances (TPM normalized read counts) and results of DESeq2 differential gene expression analysis. (Fig. 4A, figs. S10-S13, tables S6-S7). </p> <p><strong>Data S3: </strong><em>Emiliania huxleyi </em>CCMP3266 sGenome gene annotation file version 2 (GFF3 format).</p> <p><strong>Data S4: </strong>Feature quantification obtained using Compound Discoverer. The table presents the output analysis using Compound Discoverer (v3.3) with a putative identification of metabolites. Each identified metabolite (each row) contains a sub-table under the + tab (on the left side) that specifies the feature quantification in each analyzed sample. Sample ID appears in the “Study File ID” column in each sub-table. Values in the columns “Exchange Rate [%]: 0” and “Exchange Rate [%]: 1” represent relative abundances of the molecules in their unlabeled form and with single <sup>13</sup>C-label (M+1 isotopologue), respectively. Compounds marked with “1” in the “Tags” column were further validated using standards. The average M+1 isotope abundance [%] for <em>S</em>-Adenosylmethionine (SAM) and 5'-<em>S</em>-Methyl-5'-thioadenosine (MTA)—as reported in Fig. 4D—were calculated by averaging the values in the “Exchange Rate [%]: 1” column from samples supplemented with <sup>13</sup>C-labeled and unlabeled DMSP, respectively. Detailed information regarding the isotope abundances of SAM and MTA can be found in the tab “SAM and MTA isotope abundance” in the table. In the tab “Features Positive Mode” samples F10, F12, F14, and F16 were supplemented with <sup>13</sup>C-labeled DMSP and samples F2, F4, F6, F8 were supplemented with unlabeled DMSP. In the tab “Features Negative Mode” samples F2, F3, F4, and F5 were supplemented with <sup>13</sup>C-labeled DMSP and samples F10, F11, F12, F13 were supplemented with unlabeled DMSP.</p>
Dataset for publication "Sol-gel derived silicate-phosphate glass SiO2–P2O5–CaO–TiO2: the effect of titanium isopropoxide on porosity and thermomechanical stability"
<p>In this work, sol-gel silica-phosphate glasses (30 mol.% P<sub>2</sub>O<sub>5</sub>) with defined porosity from 2.5 to 7.5 mol.% TiO<sub>2</sub> modifications were synthesized. Thee dimensional stability with respect to the thermal stability of porous silica-phosphate glasses modified by titanium dioxide (TiO<sub>2</sub>) through titanium isopropoxide (TTIP) precursor was studied.</p> <p>The data of thermomechanical and thermogravimetric analysis, X-ray diffraction, SEM -EDX, mercury intrusion porosimetry, N2 physisorption, micro-computed tomography, ultra-small-angle X-ray scattering are published.</p>
Dataset (I.) related to publication: PIP4K2C inhibition reverses autophagic flux arrest induced by SARS-CoV-2
<p>MD simulation data (PIKfyve simulations) related to publication: </p> <p>Karim, M., Mishra, M., Lo, CW. <em>et al.</em> PIP4K2C inhibition reverses autophagic flux impairment induced by SARS-CoV-2. <em>Nat Commun</em> <strong>16</strong>, 6397 (2025). https://doi.org/10.1038/s41467-025-61759-1</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of PIKfyve in complex with RMC-113 [18 replicas; each 4 us] (-out.cms files and trajectories).</li> </ul> <p> </p>
Dataset related to the publication Rotbarth et al 2024, "Boreal forests are heading for an open state", PNAS
<p>This dataset contains all variables involved in data analysis in the publication Rotbarth et al 2024, "Boreal forests are heading for an open state", PNAS. </p> <p>The meta data file contains explanations for each variable in the main dataset. The second dataset includes mean annual temperatures for the years 2020-2100 based in projections from the EC-Earth3-Veg-LR model. More details on both datasets are provided in the Supplementary information Table S1. </p>
Analyse comparative du positionnement de HEPIA vis-à-vis des autres HES du domaine Ingénierie et Architecture concernant la gestion des publications scientifiques en accès ouvert
<p>The dataset includes a series of Excel files containing data relevant for a Master project " <span>Analyse comparative du positionnement de HEPIA vis-à-vis des autres HES du domaine Ingénierie et Architecture concernant la gestion des publications scientifiques en accès ouvert</span>" carried out by Valentina Caracuta at the Haute Ecole de Gestion (HEG) de Géneve a.a. 2022-2024 under the supervion of Patrick Ruch and Emilie Pasche. </p> <p>The data in this dataset allows for a comparison of the practices and strategies of different universities of applied sciences (HES) in the field of engineering and architecture, especially with regard to open access to scientific publications. </p> <p>It includes the following informations: </p> <p>List of scientific publications: Detailed data on the publications issued by the different institutions, including type of publication, year, co-authors and academic or industrial partnerships.</p> <p>Open access data: Information specific to open access publications, such as the journals and platforms used, the type of access (green, gold, etc.), and the access policy of each institution.</p> <p>Comparative publication strategies: Analysis of the strategies adopted different HES in terms of managing open access publications, including possible institutional guidelines, incentives to publish in open access, and logistical support.</p> <p>Performance and visibility indicators: Data on the visibility of publications (citations, impact of journals) and the share of articles in open access in each institution.</p>
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Raw data for publication: Simultaneous Improvement of Surface Wettability and UV Resistance of Wood with Lignin-Based Treatments
<p>These files contain:</p> <p>CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Calculations of physical test of wood samples modified with different treatments.</p> <p>Scan of samples modified with different treatments after radiation tests.</p>
Datasets and scripts for the publication "Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe1-xO) at Elevated Temperatures: Accurate force field from Deep Learning"
<div> <div> <div> <div> <p><strong>All the datasets and scripts for the publication"Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe<sub>1-x</sub>O) at Elevated Temperatures: Accurate force field from Deep Learning".</strong></p> <p>This database contains high-fidelity datasets for non-stoichiometric wüstite (Fe₁₋ₓO), including atomic coordinates, energies, and forces generated through ab initio molecular dynamics (AIMD) and refined using Deep Potential (DP) training. The dataset encompasses bulk phases, vacancy structures, and surface orientations, enabling accurate modeling of defect clusters and thermodynamic properties. It supports machine-learning force field development, offering insights into defect formation and large-scale simulations of Fe₁₋ₓO systems at elevated temperatures.</p> </div> </div> </div> </div> <div> <p>Description of the File Structure of Fe1-xO_DeepMD_Code_Datasets_Analysis.zip:</p> <p>1. `<code>init</code>` Folder <br>This folder contains the foundational datasets and inputs used for training and developing the machine-learning force field for Fe₁₋ₓO. </p> <blockquote> <p>1.1 `<code>01.train_data</code>` Subfolder <br>This folder organizes data related to the initial training of the Deep Potential (DP) model. <br>- `<code>dpmd_dataset</code>`: Processed dataset ready for DeepMD training, containing atomic configurations, forces, and energies.<br>- `<code>dpmd_rawfiles</code>`: Raw files from ab initio molecular dynamics (AIMD) simulations, serving as the source for generating training datasets.</p> <p>1.2 `<code>02.develop_data</code>` Subfolder<br>Contains `<code>.vasp</code>` files representing structural data used to develop and refine the force field. The structures include bulk, vacancy, and surface configurations of Fe₁₋ₓO. <br>- Files labeled `<code>bulk</code>` represent bulk Fe₁₋ₓO systems with varying lattice constants. <br>- Files labeled `<code>defect</code>` represent Fe and O vacancy structures (single and double vacancies). <br>- Files labeled `<code>surface</code>` represent Fe₁₋ₓO surface structures in various crystallographic orientations. <br><br></p> </blockquote> <p>2. `<code>run</code>` Folder<br>This folder contains files and logs generated during iterative training and testing of the DP force field, as well as subfolders for each iteration of the training process. </p> <blockquote> <p>2.1 Iteration Folders (`<code>iter.000000</code>` to `<code>iter.000024</code>`):<br>Each folder represents an iteration in the iterative refinement of the DP model, with three subfolders: <br>- `<code>00.train</code>`: Contains training data and outputs for the DP model during the current iteration. <br>- `<code>01.model_devi</code>`: Tracks deviations between DP predictions and ab initio results, guiding dataset selection for the next iteration. <br>- `<code>02.fp</code>`: Stores first-principles (FP) results from CP2K used to improve DP model accuracy. </p> <p>2.2 Other Key Files: <br>- `<code>cp2k.input</code>`: Input file for CP2K, used for performing ab initio calculations on configurations during the iterative process. <br>- `<code>dpdispatcher.log</code>`: Log file tracking the progress of data dispatching and task execution. <br>- `<code>dpgen.log</code>`: Log file recording operations of DPGEN during dataset generation and force field development. <br>- `<code>dpgen_nohup.sh</code>`: Script for running DPGEN in the background. <br>- `<code>machine_slurm_cp2k.json</code>`: Configuration file specifying computing resources for CP2K simulations in a cluster environment. <br>- `<code>param_cp2k.json</code>`: Parameter file for CP2K calculations, defining simulation settings. <br>- `<code>record.dpgen</code>`: Record of iterative processes, including input parameters and outputs for each stage.</p> </blockquote> <p>This organized structure ensures a systematic approach to dataset preparation, model training, and iterative refinement for developing accurate machine-learning potentials for Fe₁₋ₓO.</p> <p>graph-compress.0330.pb is the final compressed DeepMD potential parameters.</p> </div>
Paleomagnetic data associated to publication "Latest Barremian - early Aptian chronostratigraphy and sedimentary evolution of the northwestern Maestrat Basin", Geologica Acta
<p>The data set includes paleomagnetic files in 2G and rs3 format of Th and AF demagnetizations of the NRM, as well as tables with directions of the ChRM and associated data.</p>
D2.3.1 Lab demonstrator of 6GSMART private-public interworking
<p><em>D2.3.1 Lab demonstrator of 6GSMART private-public interworking</em></p> <p>Video</p> <p>Developed by TELEFÓNICA-MINSAIT (Spain)</p>
Figures referring to the paper "Radii, masses, and transit-timing variations of the three-planet system orbiting the naked-eye star TOI-396" (Bonfanti et al.) accepted for publication in A&A on 20/11/2024
<p>Fig. A.1: ttv_pla1_2.pdf</p> <p>TTV amplitudes obtained for TOI-396 b (black markers) and TOI-396 c (red markers). This is a superposition of the first two panels of Figure 4 in the paper emphasising the anti-correlation pattern.</p> <p> </p> <p>Fig. A.2: lcGLS*.pdf</p> <p>First column: Generalised Lomb-Scargle periodograms of TESS Sector 3 (lcGLS_3.pdf), 4 (lcGLS_4RawFluxrm.pdf), 30 (lcGLS_30RawFlux.pdf), and 31 (lcGLS_31RawFlux.pdf) raw photometry. Second column: Generalised Lomb-Scargle periodograms of the residuals (lcGLSRaw_3noSine.pdf, lcGLSRaw_4noSinerm.pdf, lcGLSRaw_30noSine.pdf, lcGLSRaw_31noSine.pdf) after removing the best fitting sinusoidal signals at the most significant frequency identified in the periodograms of the corresponding TESS time series. The long dashed red line marks the FAP at 0.1\%. Significant peaks are detected in the 6---8 day range, which we attribute to the presence of active regions co-rotating with the star.</p>
Public survey data of the study "Innovative and inclusive governance"
<p>The data set is based on the data of the public survey conducted in March 2023 (03.03.2023-03.15.2023). The survey was conducted to establish the respondents' political attitudes, political participation, incl. participation in the 14th Saeima elections, media usage habits, media literacy, as well as the attitudes towards Covid-19. A separate survey section is devoted to questions about higher education and science in Latvia. The sample size reached in the survey is 1016 citizens of the Republic of Latvia aged 18 and over. The anonymized data set was obtained through to the stratified sampling method by interviewing respondents in their places of residence. The survey was conducted by the SKDS Research Center on the order of researchers from the University of Latvia within the framework of the State Research Program project "Innovative and inclusive governance for the promotion of public involvement, trust, and communication".</p>
Data for publication: A food system transformation can enhance global health, environmental conditions and social inclusion
<p>Data related to the publication "A food system transformation can enhance global health, environmental conditions and social inclusion"</p>
Age Dependence of the Occurrence and Architecture of Ultra-Short-Period Planet Systems (public data release)
<p>This file includes key data and column definitions for generating figures in the manuscript. The data were collected and processed from publicly available datasets, including Kepler DR25 and the planetary systems table (https://exoplanetarchive.ipac.caltech.edu/), LAMOST DR9 (https://www.lamost.org/), Gaia DR3 (https://gea.esac.esa.int/archive/), and The California-Kepler Survey (https://california-planet-search.github.io/cks-website/). Additional data required for the manuscript can also be obtained from these publicly available datasets.</p>
Data for publication: Developing self-calibrating system for fiber Bragg grating based guided wave sensing under changing temperature conditions
<p>Data for section 3 and section 4 Figure 7- Figure 12 i n. txt format for easy viewing without need for special licenses</p> <p>The work related to the automated system was supported by the Project ‘Guided waves based reference-free SHM using fiber Bragg grating sensors (REFFREE)’<br>(2020/39/D/ST8/00188) Granted by National Science Center, Poland.</p>
D2.3.2 Lab demonstrator of 6GSMART private-public interworking
<p><em>D2.3.2 Lab demonstrator of 6GSMART private-public interworking - Cloud perspective</em></p> <p>This deliverable consists of a lab demonstrator of the software components that constitute the 6GSMART private-public interworking system.</p>
Bibliography of mycological publications for the Northwestern Siberia (Tuymen region s.l.)
Open the record for dataset details and reuse information.
Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information
<p><strong>Overview of Dataset </strong></p> <p>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. <br><br></p> <p>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.</p> <p><br><strong>File descriptions</strong></p> <p> </p> <p>Table 1. Canadian LIS authors table (authors)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>author_id</p> </td> <td> <p>Unique identifier for the publication in the LIS database</p> </td> </tr> <tr> <td> <p>first_name</p> </td> <td> <p>First name of author</p> </td> </tr> <tr> <td> <p>last_name</p> </td> <td> <p>Last name of author</p> </td> </tr> <tr> <td> <p>full_name</p> </td> <td> <p>Full name of author</p> </td> </tr> <tr> <td> <p>status</p> </td> <td> <p>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)</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 2. Works table (publications)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>pub_id</p> </td> <td> <p>Unique identifier for the publication in the LIS database</p> </td> </tr> <tr> <td> <p>doi</p> </td> <td> <p>Digital object identifiers</p> </td> </tr> <tr> <td> <p>openalex_work_id</p> </td> <td> <p>Identifier of the work in the OpenAlex database (URL format)</p> </td> </tr> <tr> <td> <p>isbn</p> </td> <td> <p>International standard book number (ISBN).</p> </td> </tr> <tr> <td> <p>doc_type</p> </td> <td> <p>Document type. Can take one of the following values: article; review; conference paper, book; edited book; book chapter.</p> </td> </tr> <tr> <td> <p>publication_year</p> </td> <td> <p>Year of publication</p> </td> </tr> <tr> <td> <p>title</p> </td> <td> <p>Title of the document</p> </td> </tr> <tr> <td> <p>source_name</p> </td> <td> <p>Title of the source (journal, conference, or book title for book chapters)</p> </td> </tr> <tr> <td> <p>author_list_full</p> </td> <td> <p>Full text listing of author names</p> </td> </tr> <tr> <td> <p>volume</p> </td> <td> <p>Volume number</p> </td> </tr> <tr> <td> <p>issue</p> </td> <td> <p>Issue number</p> </td> </tr> <tr> <td> <p>pages</p> </td> <td> <p>First and last pages separated by a hyphen.</p> </td> </tr> <tr> <td> <p>bk_edition</p> </td> <td> <p>Book edition</p> </td> </tr> <tr> <td> <p>bk_editor</p> </td> <td> <p>Name of book editor (for book chapters)</p> </td> </tr> <tr> <td> <p>publisher</p> </td> <td> <p>Publisher of the book/journal</p> </td> </tr> <tr> <td> <p>source_id</p> </td> <td> <p>Foreign key to the sources table</p> </td> </tr> <tr> <td> <p>url</p> </td> <td> <p>URL for the publication</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 3. Author publications table (authors_publications)</p> <table> <tbody> <tr> <td> <p><a name="_Hlk155194541"></a><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>author_id</p> </td> <td> <p>Unique identifier for the author in the authors table</p> </td> </tr> <tr> <td> <p>pub_id</p> </td> <td> <p>Unique identifier for the work in the publications table</p> </td> </tr> <tr> <td> <p>author_position</p> </td> <td> <p>Position on the byline.</p> </td> </tr> <tr> <td> <p>role</p> </td> <td> <p>Role of the author on the work (author/editor)</p> </td> </tr> </tbody> </table> <p>Table 4. Author IDs table (authors_ids)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>author_id</p> </td> <td> <p>Unique identifier for the author in the authors table</p> </td> </tr> <tr> <td> <p>source</p> </td> <td> <p>Source for the identifier (e.g., OpenAlex, Scopus, Google Scholar, ORCID)</p> </td> </tr> <tr> <td> <p>identifier</p> </td> <td> <p>Identifier for the author in the source database</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 5. Publication source table (sources)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>source_id</p> </td> <td> <p>Unique identifier for the source</p> </td> </tr> <tr> <td> <p>source_name</p> </td> <td> <p>Name of the source</p> </td> </tr> <tr> <td> <p>publisher</p> </td> <td> <p>Publisher name for the source</p> </td> </tr> <tr> <td> <p>issn</p> </td> <td> <p>ISSN for the source</p> </td> </tr> <tr> <td> <p>source_type</p> </td> <td> <p>OpenAlex source type (e.g., journal, conference)</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 6. Institutions table (institutions)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>institution_id</p> </td> <td> <p>Unique identifier for the institution</p> </td> </tr> <tr> <td> <p>institution_name</p> </td> <td> <p>Name of the Canadian academic institution</p> </td> </tr> <tr> <td> <p>city</p> </td> <td> <p>Name of the city in which the institution is primarily located</p> </td> </tr> <tr> <td> <p>province</p> </td> <td> <p>Two-letter code of the province in which the institution is located</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 7. Institution IDs table (institutions_ids)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>institution_id</p> </td> <td> <p>Unique identifier for the institution in the institutions table</p> </td> </tr> <tr> <td> <p>id_source</p> </td> <td> <p>Source database for the identifier (e.g., OpenAlex)</p> </td> </tr> <tr> <td> <p>identifier</p> </td> <td> <p>Identifier linked to the institution in the source database</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 8. Authorship institutional affiliation table (authors_publications_institutions)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>author_id</p> </td> <td> <p>Author component of the authorship information in the authors_publications table</p> </td> </tr> <tr> <td> <p>pub_id</p> </td> <td> <p>Publication component of the authorship information in the authors_publications table</p> </td> </tr> <tr> <td> <p>institution_id</p> </td> <td> <p>Unique identifier for the affiliated institution in the institutions table</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 9. Citations table (citations)</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>citing_pub_id</p> </td> <td> <p>Unique identifier for the citing work in the publications table</p> </td> </tr> <tr> <td> <p>cited_pub_id</p> </td> <td> <p>Unique identifier for the cited work in the publications table</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>To submit updates </strong></p> <p>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.<br><br><strong>Data paper</strong><br><br>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>.</p> <p><br><br><strong>How to cite this dataset</strong></p> <p>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> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.