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1,551 results for “Availability”
Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution
<p>Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054247">NRCS Soil Survey Laboratory Methods Manual</a>), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <a href="https://doi.org/10.5281/zenodo.2609113"><strong>here</strong></a>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>available.water.capacity = available water capacity in mm,</li> <li>usda.mm = determination method: Water Retention Difference in mm,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
King Louie: DBMS Availability Evaluation Data Sets
<p>These data sets provide all availability measurements as accompanying material for the research paper <strong><em>King</em> </strong><em><strong>Louie: Reproducible Availability Benchmarking of Cloud-hosted DBMS</strong> </em>that is presented in the 35th ACM/SIGAPP Symposium on Applied Computing (SAC ’20), March 30-April 3, 2020, Brno, Czech Republic.</p> <p> </p> <p> </p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Data and Code open availability for the FRETsael paper
<p>This dataset includes the raw files of the measurements of Actin-Myosin interactions in SH-SY5Y cells, as well as the Matlab code for the simulations and analyses used in the paper about FRETsael: "<strong>FRET-sensitized acceptor emission localization (FRETsael) – nanometer localization of biomolecular interactions using fluorescence lifetime imaging</strong>"</p>
GDC-PANCAN.htseq_counts and associated meta-data no longer available from gdc.xenahubs.net
<p>The RNA-seq data and associated meta-data I used for my publications, downloaded from gdc.xenahubs.net but no longer available in this form (i.e. raw counts) from the website. </p>
A time series database of available P concentrations and grass growth in soils receiving DPW fertilizers
<p>This data set contains a time-series database of available and exchangeable P concentrations and grass dry matter yield in soils receiving struvites and hydrochar produced from DPS</p>
Availability of information on citizen science activities, checked against the Activities & Dimensions Grid of Citizen Science on the basis of some projects
<p>The research resulting in this report aimed at answering the following questions:</p> <ul> <li> <p>Which information on citizen science activities is online available that matches the Activity & Dimension Grid of Citizen Science or goes beyond it? </p> </li> <li> <p>Is there any contradictory information?</p> </li> <li> <p>What can be the reason for the availability or non-availability of information about citizen science activities?</p> </li> <li> <p>How does/could this impact on the CS Track’s recommendations?</p> </li> </ul> <p>The corresponding dataset consists of the results of a keyword-based search in the WP2 project database. The information retrieval resulted in 3318 projects on which information is available in German or English.</p> <p>More information on this research can be found in D2.2 section 3.2.</p>
Supplementary data for "Influence of prey availability on habitat selection during the non-breeding period in a resident bird of prey"
<p><strong>Abstract</strong></p> <p>Background: For resident birds of prey in the temperate zone, the cold non-breeding period can have strong impacts on survival and reproduction with implications for population dynamics. Therefore, the non-breeding period should receive the same attention as other parts of the annual life cycle. Birds of prey in intensively managed agricultural areas are repeatedly confronted with unpredictable, rapid changes in their habitat due to agricultural practices such as mowing, harvesting, and ploughing. Such a dynamic landscape likely affects prey distribution and availability and may even result in changes in habitat selection of the predator throughout the annual cycle.</p> <p>Methods: In the present study, we 1) quantified barn owl prey availability in different habitats across the annual cycle, 2) quantified the size and location of barn owl breeding and non-breeding home ranges using GPS-data, 3) assessed habitat selection in relation to prey availability during the non-breeding period, and 4) discussed differences in habitat selection during the non-breeding period to habitat selection during the breeding period.</p> <p>Results: The patchier prey distribution during the non-breeding period compared to the breeding period led to habitat selection towards grassland during the non-breeding period. The size of barn owl home ranges during breeding and non-breeding were similar, but there was a small shift in home range location which was more pronounced in females than males. The changes in prey availability led to a mainly grassland-oriented habitat selection during the non-breeding period. Further, our results showed the importance of biodiversity promotion areas and undisturbed field margins within the intensively managed agricultural landscape. </p> <p>Conclusions: We showed that different prey availability in habitat categories can lead to changes in habitat preference between the breeding and the non-breeding period. Given these results we show how important it is to maintain and enhance structural diversity in intensive agricultural landscapes, to effectively protect birds of prey specialised on small mammals. Hereafter we provide the datasets and R script to reproduce the resource selection functions.</p>
African Savanna grasses outperform trees across the full spectrum of soil moisture availability
<p>Summary</p> <ul> <li>Models of tree-grass coexistence in savannas make different assumptions about the relative performance of trees and grasses under wet vs. dry conditions. We quantified transpiration and drought tolerance traits in 26 tree and 19 grass species from the African savanna biome across a gradient of soil water potentials to test for a tradeoff between water use under wet conditions and drought tolerance.</li> <li>We measured whole-plant hourly transpiration in a growth chamber and quantified drought tolerance using leaf osmotic potential (Ψ<sub>osm</sub>). We also quantified whole-plant water use efficiency (WUE) and relative growth rate (RGR) under well-watered conditions.</li> <li>Grasses transpired twice as much as trees on a leaf-mass basis across all soil water potentials. Grasses also had a lower Ψ<sub>osm</sub> than trees, indicating higher drought tolerance in the former. Higher grass transpiration and WUE combined to largely explain the threefold RGR advantage in grasses.</li> <li>Our results suggest that grasses outperform trees under a wide range of conditions, and that there is no evidence for a trade-off in water use patterns in wet vs. dry soils. This work will help inform mechanistic models of water use in savanna ecosystems, providing much-needed whole-plant parameter estimates for African species.</li> </ul>
NHS England COVID-19 Exposure Notification App and Test Availability Data
<p>This dataset was scraped from the API serving the NHS COVID-19 App for England and Wales, and the NHS COVID-19 test availability service.</p> <p>It contains the following files:</p> <p><strong>exposure_keys.csv</strong><br> Metadata associated with the published exposure keys for the Bluetooth Google/Apple Exposure Notification (GAEN) system. The actual broadcast keys were not collected, only the metadata attached to them. The columns in the table match those in the <a href="https://developers.google.com/android/exposure-notifications/exposure-key-file-format">exposure key export format</a>, with the exception of the "export_date" field which is the "end_timestamp" of the key export in which that key was first seen. This file is believed to be complete between 2020-09-13 and 2023-04-29 when the NHS COVID-19 app was retired.</p> <p><strong>exposure_configuration.csv</strong><br> This table contains the NHS COVID-19 App's exposure configuration JSON file fetched from the API, with a new record inserted whenever this changed. History of this file is also available in the <a href="https://github.com/ukhsa-collaboration/covid19-app-system-public">app's git repository</a>, and entries in this file from before 2021-07-11 were imported from there. The timestamps for entries dated since that point will match the time that the configuration was published to the API, which may not be the case for the git repository as this was normally updated after a delay.</p> <p><strong>risky_venues.csv</strong><br> "Risky venue" notification data for the COVID-19 App. This was an NHS App-specific feature, not part of the GAEN specification, which allowed users to "check in" to a venue and receive a notification if they were present at the same time as someone who subsequently tested positive for COVID-19. This file is believed to be complete between 2020-09-24 and 2022-02-22 (when it appears the "risky venue" feature was retired), with a known data collection gap between 2021-08-03 and 2021-08-06.</p> <p><strong>walk_in_pcr_availability.csv</strong><br> Walk-in PCR test availability for the entire UK, used by the NHS PCR test booking service. This contains JSON objects provided by the API, which are broken down by region. A new row was inserted whenever this JSON object changed. This file is believed to be complete between 2021-12-27 and 2022-03-30 (after which it appears the online test booking service was retired).</p> <p><strong>home_test_availability.csv</strong><br> Home test (PCR or Lateral Flow Device) availability, for the public and for "key workers" who had priority ordering PCR tests. A new row was inserted whenever the availability changed. This file is believed to be complete between 2021-12-27 and 2022-08-22 when the data ended.</p> <p> </p> <p>The final version of the source code used to fetch this data is <a href="https://doi.org/10.5281/zenodo.7883754">available here</a>.</p>
Unveiling metastable ensembles of GRB2 and the relevance of interdomain communication during folding - available data.
<p>The folding process of multidomain proteins is a highly intricate phenomenon involving the assembly of distinct domains into a functional three-dimensional structure. During this process, each domain may fold independently while interacting with other domains to form a functional protein. The folding of multidomain proteins can be influenced by various factors, including the composition and structure of each domain or the presence of disordered linker regions, as well as the surrounding environment. Misfolding of multidomain proteins can lead to the formation of non-functional structures associated with a range of diseases, including cancers and neurodegenerative disorders. Understanding this process is an essential step for many biophysical analyzes, such as stability, interaction, malfunctioning, and rational drug design. One such multidomain protein is the growth factor receptor-bound protein 2 (GRB2), an adaptor protein essential in regulating cell survival. GRB2 consists of one central Src Homology 2 (SH2) domain flanked by two Src Homology 3 (SH3) domains. The SH2 domain interacts with phosphotyrosine regions in other proteins, while the SH3 domains recognize proline-rich regions on protein partners during cell signaling. In this study, we combined computational and experimental techniques to investigate the folding process of GRB2. We sampled the conformational space through computational simulations and mapped the mechanisms involved by calculating free energy profiles, indicating possible intermediate states. From the molecular dynamics and trajectories, we used the Energy Landscape Visualization Method (ELViM), which allowed us to visualize a three-dimensional representation of the overall energy surface. We identified two possible parallel folding routes that cannot be seen in a one-dimensional analysis, with one occurring more frequently during folding. Supporting these results, we used DSC and fluorescence spectroscopy techniques to confirm these intermediate states in vitro. Finally, we analyzed the deletion of domains to compare our model outputs with previously published results, supporting the presence of interdomain modulation. Overall, our study highlights the significance of interdomain communication within the GRB2 protein and its impact on the formation, stability, and structural plasticity, which are crucial for its interaction with other proteins in key signaling pathways.</p> <p> </p>
Shear modulus reduction and damping ratio curves collected from multiple literature sources available in Italy
<p><em>Data</em> are 485 G/G<sub>0 </sub>(γ) and damping ratio, D(γ) curves collected from multiple literature sources available in Italy. Each curve was associated with the related engineering geological units considered in seismic microzonation studies. The data focus on providing reference information that can serve as key data for large-scale hazard assessments worldwide and to guide the translation of information from the laboratory scale to the scale of geological-engineering cross-sections.</p>
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>). </p> <p> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
DFT Featurization of 730 Commercially Available Boronic Acids
<p>Dataset of DFT calculated features of 730 commercially available boronic acids. </p> <p>Software, scripts, input data, and workflow:</p> <p>https://github.com/Gademann-UZH/Chemical-Space-Generation</p> <p>Archived version, see</p> <p>DOI: 10.5281/zenodo.7540235</p> <p>Publication, see:</p> <p>Mechanistic Studies and Data Science-Guided Exploration of Bromotetrazine Cross-Coupling<br> Lukas V. Hoff, Gleb A. Chesnokov, Anthony Linden, and Karl Gademann<br> ACS Catalysis 2022 12 (15), 9226-9237<br> DOI: 10.1021/acscatal.2c01813 </p> <p> </p> <p>V1.1: Corrected calculations for OB(O)C1=CC=C(C=C1)C(=C(/C1=CC=CC=C1)C1=CC=C(C=C1)B(O)O)\C1=CC=CC=C1 and for OB(O)C1=CC(F)=C(OCCCN2CCOCC2)C=C1.</p>
Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESULTS DATASET (with Mega Journals)
<p>The dataset contains all the data produced running the research software for the study:"Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta".</p> <p>Disclaimer: these results are not considered to be representative, because we have fount that Mega Journals skewed significantly some of the data. The result datasets without Mega Journals are published <a href="https://zenodo.org/record/8249907">here</a>.</p> <p>Description of datasets:</p> <ul> <li><strong>SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: </strong>containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> <li><strong>SSH_Publications_by_Discipline.csv:</strong> containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>SSH_Publications_and_Journals_by_Country:</strong> containing information about number of publications and journals per country. The dataset has three columns, the first, labeled <strong>"Country",</strong> contains single countries of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>result_disciplines.json:</strong> the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.</li> <li><strong>result_countries.json:</strong> the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.</li> <li><strong>duplicate_omids.csv: </strong>a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "<strong>OC_omid"</strong>, the internal OC Meta identifier; "<strong>issn", </strong>the issn values associated to that identifier</li> <li><strong>eu_data.csv: </strong>contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"Original_Title"</strong>,<strong> "Country_of_Publication"</strong>,<strong>"ERIH_PLUS_Disciplines"</strong>, <strong>"disc_count"</strong>, the number of disciplines per Journal.</li> <li><strong>eu_disciplines_count.csv: </strong>containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>meta_coverage_eu.csv: </strong>contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> <li><strong>us_data.csv: </strong>contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"Original_Title"</strong>,<strong> "Country_of_Publication"</strong>,<strong>"ERIH_PLUS_Disciplines"</strong>, <strong>"disc_count"</strong>, the number of disciplines per Journal.</li> <li><strong>us_disciplines_count.csv: </strong>containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>meta_coverage_us.csv: </strong>contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> </ul> <p> </p> <p><strong>Abstract of the research: </strong></p> <p><strong>Purpose:</strong> this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research.<br> <strong>Study Design and Methodology:</strong> the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data.<br> <strong>Findings:</strong> the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA.<br> <strong>Originality:</strong> this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.</p> <p><strong>Related resources:</strong></p> <p>Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. <a href="https://doi.org/10.5281/zenodo.8263908">https://doi.org/10.5281/zenodo.8263908</a></p> <p>Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. <a href="https://doi.org/10.5281/zenodo.8174644">https://doi.org/10.5281/zenodo.8174644</a></p> <p>Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (<a href="https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5">https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5</a>)</p>
Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESULTS DATASET (without Mega Journals)
<p>The dataset contains all the data produced running the research software for the study <em>Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta</em>, a research carried out in the contest of the Open Science course 22/23 at the University of Bologna.</p> <p>Mega Journals have been excluded form the datasets, since we found they were significantly skewing the results, the only datasets not interested by this exclusion are <strong>SSH_Publications_in_OC_Meta_and_Open_Access_status </strong>and<strong> duplicate_omids.</strong> The result datasets with Mega Journals included are published <a href="https://doi.org/10.5281/zenodo.8250858">here</a><br> The Journals excluded from the results are: PLOS ONE (issn:1932-6203), PNAS (issn:1091-6490), Science (issn:1095-9203), Nature(issn:0028-0836).</p> <p>Description of datasets:</p> <ul> <li><strong>SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: </strong>containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> <li><strong>SSH_Publications_by_Discipline.csv:</strong> containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>SSH_Publications_and_Journals_by_Country:</strong> containing information about number of publications and journals per country. The dataset has three columns, the first, labeled <strong>"Country",</strong> contains single countries of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>result_disciplines.json:</strong> the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.</li> <li><strong>result_countries.json:</strong> the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.</li> <li><strong>duplicate_omids.csv: </strong>a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "<strong>OC_omid"</strong>, the internal OC Meta identifier; "<strong>issn", </strong>the issn values associated to that identifier</li> <li><strong>eu_data.csv: </strong>contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"Original_Title"</strong>,<strong> "Country_of_Publication"</strong>,<strong>"ERIH_PLUS_Disciplines"</strong>, <strong>"disc_count"</strong>, the number of disciplines per Journal.</li> <li><strong>eu_disciplines_count.csv: </strong>containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>meta_coverage_eu.csv: </strong>contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> <li><strong>us_data.csv: </strong>contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"Original_Title"</strong>,<strong> "Country_of_Publication"</strong>,<strong>"ERIH_PLUS_Disciplines"</strong>, <strong>"disc_count"</strong>, the number of disciplines per Journal.</li> <li><strong>us_disciplines_count.csv: </strong>containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled <strong>"Discipline",</strong> contains single disciplines of the ERIH classificaton, the second and the third, labeled <strong>"Journal_count" </strong>and <strong>"Publication_count", </strong>respectively, the number of Journals and the number of Publications counted for each discipline.</li> <li><strong>meta_coverage_us.csv: </strong>contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "<strong>EP_id", </strong>the internal ERIH PLUS identifier; <strong>"Publications_in_venue", </strong>the<strong> </strong>numbers of Publications counted in each venue; <strong>"</strong><strong>OC_omid", </strong>the internal OpenCitations Meta identifier for the venue;<strong> "issn",</strong> numbers of publications in each venue;<strong> "Open Access",</strong> a value to represent if the journal is OA or not, either "True" or "Unknown".</li> </ul> <p> </p> <p><strong>Abstract of the research: </strong></p> <p><strong>Purpose:</strong> this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research.<br> <strong>Study Design and Methodology:</strong> the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data.<br> <strong>Findings:</strong> the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA.<br> <strong>Originality:</strong> this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.</p> <p><strong>Related resources:</strong></p> <p>Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. <a href="https://doi.org/10.5281/zenodo.8263908">https://doi.org/10.5281/zenodo.8263908</a></p> <p>Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. <a href="https://doi.org/10.5281/zenodo.8174644">https://doi.org/10.5281/zenodo.8174644</a></p> <p>Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (<a href="https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5">https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5</a>)</p>
Dataset Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere
<p>This dataset is related to the paper "<strong>Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial <em>Pseudomonas </em>in the wheat rhizosphere</strong>" (Garrido-Sanz et al., 2023, doi: 10.1186/s40168-023-01660-5) and contains the data obtained from bacterial competition asays and plant-growth measurements.</p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive (RSA) under the BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA948847">PRJNA948847</a>.</p> <p>The R script used to analyze the data generated in the paper is available at <a href="https://github.com/dgarrs/Pprotegens_proliferation_NatComs">GitHub </a>and <a href="https://doi.org/10.5281/zenodo.8322086">Zenodo</a>.</p>
Urbanization and fragmentation have opposing effects on soil nitrogen availability in temperate forest ecosystems.
Nitrogen (N) availability relative to plant demand has been declining in recent years in terrestrial ecosystems throughout the world, a phenomenon known as N oligotrophication. The temperate forests of the northeastern U.S. have experienced a particularly steep decline in bioavailable N, which is expected to be exacerbated by climate change. This region has also experienced rapid urban expansion in recent decades that leads to forest fragmentation, and it is unknown whether and how these changes affect N availability and uptake by forest trees. Many studies have examined the impact of either urbanization or forest fragmentation on nitrogen (N) cycling, but none to our knowledge have focused on the combined effects of these co-occurring environmental changes. We examined the effects of urbanization and fragmentation on oak-dominated (Quercus spp.) forests along an urban to rural gradient from Boston to central Massachusetts (MA). At eight study sites along the urbanization gradient, plant and soil measurements were made along a 90 m transect from a developed edge to an intact forest interior. Rates of net ammonification, net mineralization, and foliar N concentrations were significantly higher in urban than rural sites, while net nitrification and foliar C:N were not different between urban and rural forests. At urban sites, foliar N and net ammonification and mineralization were higher at forest interiors compared to edges, while net nitrification and foliar C:N were higher at rural forest edges than interiors. These results indicate that urban forests in the northeastern U.S. have greater soil N availability and N uptake by trees compared to rural forests, counteracting the trend for widespread N oligotrophication in temperate forests around the globe. Such increases in available N are diminished at forest edges, however, demonstrating that forest fragmentation has the opposite effect of urbanization on coupled N availability and demand by trees.
Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: I - Percent Cover Data 2013
This dataset examines shifts in plant community structure along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Percent cover data was collected for subcanopy vascular and nonvascualr vegetation in July 2013.
Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: II - Nitrogen Data 2013
This dataset examines shifts in extractable soil pore water chemistry along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Measured parameters include: dissolved inorganic N (DIN), dissolved organic N (DON), free amino acids, total dissolved N (TDN), soil temp. at 10 cm, seasonal ice depth, and volumetric soil moisture at 10 cm. Soil pore water samples and environmental variables were collected every three to four weeks from late June to late September, 2013 for a total of five sampling events.
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