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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>
Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data
<p>Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data</p> <p>Data provided consists of:</p> <p>raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406)</p> <p>simulated photon counting data and the processed results</p> <p> </p>
Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)
<p>This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener"><em><u>Prediction of global wheat cultivation distribution under climate change and socioeconomic development</u></em></a>" which was published in The Science of The Total Environment. The present study represents a significant extension of our previous research on "<em><a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a></em>.</p> <p>Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change.</p> <p>We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting.</p> <p>The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets.</p> <p>Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios.</p> <p>The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa.</p> <p>Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution.</p> <p>We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics.</p> <p>The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences.</p> <p>The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data).</p> <p><strong>code: </strong></p> <p>This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information.</p> <p>The exampleData contains</p> <p>1. h_pri.tif: prior data</p> <p>2. h_res.tif: global C3 crop cultivation proportion</p> <p>Run the main programme: cross_entroy.m</p> <p><strong>data: </strong></p> <p>This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format.</p> <p><strong>1 Global distribution of the long-term wheat-</strong><strong>c</strong><strong>ultivation area fraction: </strong></p> <p>This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated.</p> <p><strong>r2s1f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario</p> <p><strong>r4s2f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario</p> <p><strong>r8s3f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario</p> <p><strong>2 </strong><strong>S</strong><strong>patial overlap between the long-term period of land suitability for wheat </strong><strong>planting </strong><strong>and wheat cultivation distribution: </strong></p> <p>This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:<strong>{1, 2, 3}</strong>, <strong>1</strong> wheat cultivation existed but was predicted to be unsuitable to plant wheat; <strong>2 </strong>presented a reduction in the wheat cultivation area compared to the land's suitability; <strong>3</strong> presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat.</p> <p><strong>com_suit_fra126.tif: </strong>the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6</p> <p><strong>com_suit_fra245.tif: </strong>the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5</p> <p><strong>com_suit_fra385.tif:</strong> the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5</p> <p><strong>3 Differences in the proportion of long-term wheat </strong><strong>c</strong><strong>ultivation: </strong></p> <p>This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability.</p> <p><strong>r2s1_f.tif:</strong> Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario</p> <p><strong>r4s2_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario</p> <p><strong>r8s3_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario </p> <p><strong>References:</strong></p> <p>Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318.</p> <p>Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481.</p> <p>For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014).</p> <p>Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x.</p> <p>Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002</p>
Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets
<p>We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need).<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. </p> <p>7 bids lists are available in this dataset. </p> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Distribution of invasive alien species of Union concern (Regulation (EU) 1143/2014) in Belgium for the reporting period 2015-2018
<p><strong>Aims and scope</strong></p> <p>Member State authorities are required to report on the distribution in their territory of each of the invasive alien species (IAS) of Union concern. These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). This distribution represents the official reporting under Article 24(1) of R.1143/2014 on invasive alien species for the period 2015–2018. Baseline distribution of these species has previously been reported and published (Adriaens et al. 2018, ).</p> <p>Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (9%), citizen science observatories (68%) and a range of other sources (23%) such as governmental agencies, water managers etc. More specifically the dataset includes:</p> <ul> <li>The citizen science recording portals www.waarnemingen.be and www.observation.be which has a specific alert system for IAS where nature volunteers can report their observations (Adriaens et al. 2018);</li> <li>Data from the Research Institute for Nature and Forest (INBO), the Flemish government institute that coordinates N2000, WFD and BIrd Directive and IAS monitoring in the terrestrial, estuarine and freshwater environment;</li> <li>Data from the Flemish Environment Agency which performs management of muskrat and invasive water plants in Flanders, gathered with a dedicated smartphone app since 2015;</li> <li>Data from the Flemish provinces and Rato vzw that manage water plants, muskrat, giant hogweed etc.;</li> <li>Some smaller datasets from cities;</li> <li>Data from the Brussels Capital Region from the Brussels Environment data portal;</li> <li>Plant inventories of the ‘contrats de rivière’ along watercourses in Wallonia, making use of a dedicated application to collect data directly from the field (fulcrum);</li> <li>The government reporting portals for IAS of the ‘Observatoire wallon de la flore, de la faune et des habitats (Service Public de Wallonie)’;</li> <li>Some validated data from specific datasets on gbif (iNaturalist, Natusfera, Naturgucker).</li> </ul> <p>Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). The mapping was assisted by dedicated software (SMARTIE) which was specifically written for the purpose of aggregating IAS data from various sources. Appropriate selection of records was performed based on the cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA). The technical format is in line with the <a href="http://cdr.eionet.europa.eu/help/ias_regulation/material/IAS-species-distribution-user-manual">guidelines</a> provided to the member states for the compilation of reports on Species Distribution (SD) of Invasive Alien Species of Union concern.</p> <p><strong>File description</strong></p> <p>The dataset contains a shapefiles (<em>T1_Belgium_Union_List_Species.shp</em>) with the distribution of the species of Union Concern at 10km<sup>2</sup> (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level. The attributes table contains <em>Cellcode </em>(ETRS<sup> </sup>grid cell code) and <em>Species </em>(scientific name + authority).</p> <p><strong>Date range</strong></p> <p>The data reflects the distribution of the IAS of Union concern in Belgium in the first reporting period for the EU Regulation hence comprises observations of Union List invasive species between January 2015 (2015-01-01) and December 2018 (2018-12-31). </p> <p><strong>Validation procedure</strong></p> <p>Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account unless for Chinese mitten crab <em>Eriocheir sinensis</em>, ruddy duck <em>Oxyura jamaicensis</em>, raccoon <em>Procyon lotor</em>, Siberian ground squirrel <em>Tamias sibiricus</em>, sacred ibis <em>Threskiornis aethiopicus</em>, Egyptian goose <em>Alopochen aegyptiaca, </em>Himalayan balsam <em>Impatiens glandulifera</em>, giant hogweed <em>Heracleum mantegazzianum, </em>muskrat <em>Ondatra zibethicus </em>and red-eared slider <em>Trachemys spp</em>. For these species, it was assumed all records were correct as they originate from dedicated sampling (<em>E. sinensis</em>) within research projects, were gathered by public bodies (e.g. muskrat), or represent species that are readily recognizable by people in the field. Data provided by EASIN in the care package and GBIF data were carefully checked.</p> <p>A visual check was performed on the resulting distribution maps by representatives of the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species.</p> <p><strong>Data providers</strong></p> <p>The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF).</p>
Invasive species and thermal squeeze: Distribution of two invasive predators and drivers of ship rat (Rattus rattus) invasion in mid-elevation Fuscospora forest
This data package is from a trapping network set up in Craigieburn Forest Park, New Zealand, in 2013. These are records of the stoats and rats caught in the traps each time the traps were checked by volunteers since 2013. Associated long term air temperature and seedfall data from the Craigieburn area is also provided. If the original trapping records (containing more mammalian catch information such as weasels and cats) are required please contact the data providers.
High-frequency water temperature, chlorophyll fluorescence, wind speed, and photosynthetically active radiation data for 18 globally-distributed lakes 2008 - 2013
Abstract: This dataset was used in the analysis described in the manuscript by Rusak, J. A.J. Tanentzap, J.L. Klug, K. Rose, L.A. Winslow R. Smyth, E. Jennings, D. Pierson, S. Hendricks, A. Laas, E. Ryder, D. White, R. Adrian, L. Arvola, E. de Eyto, H. Feuchtmayr, M. Honti, V. Istanovics, I. Jones, C. McBride, S. Schmidt, G. Zhu. Wind and trophic status explain the temporal and spatial variability of chlorophyll in lakes. In review: Limnology and Oceanography Letters. The variation in chlorophyll fluorescence from 18 globally distributed lakes, was tested at monthly, daily and hourly scales in related to high-frequency measurements of wind, water temperature and radiation within lakes as well as lake productivity and morphometry among lakes. Overall, monthly variation in algal biomass was greater than that expressed at either daily or hourly scales but, combined, these latter time scales were equivalent to seasonal variation. Among lakes, algal biomass variation increased with trophic status while, within-lake variation increased with increasing wind speed variation. Together, our results suggest that predicted changes associated with a changing climate, as well as widespread ongoing cultural eutrophication, have the potential to substantially alter the variability of algal biomass and thus the predictability of the services it provides. This dataset includes the data used in the analysis described above.
NEON Biorepository Soil Collection (Distributed Periodic) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains air-dried soil samples collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: sls_bgcSubsampling_in.bgcArchiveID). Soil biogeochemical samples are collected once every 5 years, with three unique sampling locations per plot and ten plots per site. Soil sampling is conducted to a maximum depth of 30 ± 1 cm where possible. When organic (O) and mineral (M) horizons are present within a single profile they are separated prior to analysis and archiving. However, other sub-horizons are not separated. Soil from the O horizon is homogenized and non-soil material is removed by hand (no sieving), whereas soil from the M horizon is homogenized and sieved to 2 mm. Prior to archiving, all soil samples are air-dried, then placed into glass jars and stored at room temperature. See links below for NEON data products that provide various physical, chemical, and biological measurements (pH, moisture, carbon and nitrogen content and stable isotopes, inorganic nitrogen pools and net transformation rates, microbial community composition and biomass) for these same soils. In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical properties, distributed initial characterization (DP1.10047.001) and Soil physical and chemical properties, Megapit (DP1.00096.001).
Relyea, R. A., and E. E. Werner. 2000. Morphological plasticity of four larval anurans distributed along an environmental gradient. Copeia 2000:178-190.
We investigated morphological plasticity to the presence of predators in the tadpoles of four ranid frog species distributed along a pond hydroperiod gradient in southeast Michigan. We first reared all four species (Wood Frog, Rana sylvatica; Leopard Frog R. pipiens; Green Frog, R. clamitans; and Bullfrog, R. catesbeiana) under identical laboratory conditions in the presence and absence of caged larval dragonflies (Anax spp.). We then reared wood frog and leopard frog in outdoor mesocosms to examine the predator-induced responses during ontogeny. Finally, we reared leopard frog with predators fed either leopard frog or wood frog larvae to determine whether prey responses depended upon predators consuming conspecific prey. All four ranids exhibited some degree of morphological change in the presence of Anax; these differences were species specific and fairly robust to different experimental conditions. The responses over ontogeny indicated that the changes were direct responses to the predator’s presence and not an indirect result of the predator slowing anuran growth or development. Finally, larval leopard frog responded similarly to predators feeding on conspecifics and congenerics. Taken together, these results suggest that morphological responses to predators may be relatively common in larval anurans. Further, because many of the responses are known to be adaptive antipredator strategies, predator-induced morphological plasticity has important evolutionary and ecological implications.
Spider web distribution and characteristics in Dean Creek Marsh, Sapelo Island, Georgia, USA, October 2024
Salt marshes are a rare environment but are nonetheless home to many web-building spiders. This dataset describes a small-scale study of the distribution and sizes of webs in Dean Creek Marsh, located on the southern end of Sapelo Island, Georgia, USA. The study contained three components: A transect survey to understand the spatial distribution and density of spider webs among different vegetation types, a targeted search for webs to understand the population of webs in the region, and a sticky-trap study to investigate the prey abundance among vegetation types in the marsh.
Distribution and abundance of canopy trees in floodplain forests of the Wisconsin River 1999 - 2001
The Wisconsin River Floodplain Project aimed to identify landscape indicators that are well correlated with specific aspects of ecological function. This is a crucial research need requiring an integrated approach that combines landscape monitoring with field studies. Large river-floodplain systems are among the most diverse and dynamic landscapes in the US, providing many important societal values, but relatively little effort has been devoted to development and testing of landscape indicators for these systems. We developed and tested ecological indicators for large river-floodplain landscapes along reaches of the Wisconsin River to determine which landscape metrics are most useful for monitoring population, community and ecosystem processes in large river-floodplain landscapes. Spatially extensive field sampling was combined with landscape analysis in nine reaches of the Wisconsin River sampling to quantify the ability of landscape indicators to predict ecological variables over broad scales. Landscape indicators were evaluated by their utility for detecting changes in the structure and function of the Wisconsin River floodplain landscape that were related to modification of the natural flow regime, historical land use, and current land-use patterns. Our field studies were concentrated in floodplain forest in nine 12 to 20-km reaches along the lower 400 km of the Wisconsin River.
Experiment on Competition and the Local Distribution of the Grass Stipa neomexicana, Arizona, 1979 - 1986
This dataset contains the results on an experiment whose aim was to determine whether competitive displacement accounted for a species' local distribution. Within a grassland in southern Arizona, Stipa neomexicana, a C3 grass, was found to occur only on dry ridge crests with low total grass cover, while total grass cover is greater below the ridge crests in moister, low—lying areas. It was hypothesized that Stipa neomexicana was limited to these dry ridges by competitive exclusion. This hypothesis was tested by removal experiments conducted at three positions along the topographic gradient. The responses of Stipa were compared with those of Aristida glauca and other neighboring grass species, all of which are C4 grasses (Stipa being the only C3 grass in that area). This experiment was carried out from 1980 to 1983, in a grassland near Sonoita, Santa Cruz County, Arizona, USA.
The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA, 2013 - 2018
Centaurea stoebe (Asteraceae; spotted knapweed) is an emerging invader in northeast US, and is a major invasive plant in the northern Midwest and western USA. Although it has been present in New York State (NYS) for over 100 years, its apparent recent population increases and spread provide a rare opportunity to study a plant in the early stages of invasion. Therefore, a study was carried out understand how distinct environmental factors influence the distribution, density and change in density C. stoebe at different spatial scales within its novel range in the northeastern USA. First, we collected field data on the occurrence, density and change in density of this species in North Eastern United States, from 2013 to 2014. Then, using species distribution models, we assessed the potential influence of environmental factors on the invasion of spotted knapweed in northeast US. Within different parts of C. stoebe‘s range, different factors explained its occurrence, density and change in density over 2 years. Across northeast US, climate and soil factors were the most influential predictors explaining C. stoebe‘s distribution, while within Long Island in southeastern NYS and the Adirondack Mountains in northern NYS, precipitation and disturbance respectively were the most important. These results are published in the paper titled The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA (Akin-Fajiye and Gurevitch, 2018).
Spatial and temporal distribution and abundance of moths in the Andrews Experimental Forest, 1994 to 2008
The distribution and abundance of macromoth species is strongly influenced by geographical (region-neighboring plots) scale, elevation, aspect, plant community, management regime, and time of year. Noctural macromoths have been observed at a total of 263 sample sites throughout the Andrews Forest watershed since 1994. Only a limited subset of these sites is sampled each year. From 2004 to 2008, 20 sites were sampled consistently using a hierarchical sampling design stratified by elevation and vegetation type. Moths are sampled using blacklight traps deployed for one night every two weeks at each site from April through October. A total of 503 species have been observed, and approximately 300 species may be observed in any given year. The watershed can be divided into 13 distinct zones. The northwest ridge above the Andrews headquarters has the highest number of species (n = 321) and the lowest number of species occurred at upper Lookout Creek (n = 239). Each of 13 zones is missing ca. 200 of the 500 resident species, suggesting that heterogeneity in the landscape is important. A breakdown of the species into functional groups based on larval feeding habits: conifers, hardwood, herb, mix, unknown shows that 43% of Andrews species rely on a hardwoods and 63% rely on hardwoods and herbaceous angiosperms. Conifer-feeders only represent 8% of moth species. However, moths associated with conifer hosts are the most abundant; for instance, in the zone representing the midlevel of Carpenter Mountain 67% of moth individuals are conifer feeders, but only 14% of the species feed on conifers. In contrast, within the zone represented by the Headquarters site, only 32% of the individual moths feed on conifers whereas 56% feed on hardwoods. Moth biogeographic zones correspond to elevation zones and to potential vegetation.
Spatial and temporal distribution and abundance of butterflies in the Andrews Experimental Forest, 1994-1996
This database contains information on species abundance according to date and location within the H.J. Andrews Experimental Forest Lookout Creek watershed. The database provides the information needed to assess patterns in the abundance of butterflies across time and space. The distribution and abundance of butterfly species on the Andrews Forest is strongly influenced by geographical scale, elevation, aspect, plant community, management regime, and time of year. Patterns of distribution and abundance are based on an historical total of 80 species, of which 73 are resident species and about 55 of which may be observed in any given year. Butterflies were surveyed at two- week intervals from late April through early October over a three-year period (1994-6). Approximately one-third of the watershed was covered during each visit, thus each area was sampled at about 6 week intervals within each sample season.
Role of vegetation and coarse wood debris on soil processes and mycorrhizal mat distribution patterns at the Hi-15, Andrews Experimental Forest, 1994-1995
The main objective of this study was to determine if there were relationships between forest floor attributes such as the location of: (1) individual trees, (2) clusters of undergrowth vegetation, (3) coarse woody debris, (4) rocks and (5) topography and both soil characteristics and distribution patterns of ectomycorrhizal fungal mats. This data set includes mat, rock, wood, and moss distribution patterns (as presence or absence at each sampling node) as well as basic soil date taken at the same locations. The forest floor attributes were digitized using Esri ArcGIS. These GIS data layers are available as separate files in FSDB Database code SP029.
Soil aggregate size distribution and particulate organic matter content from Arctic LTER moist acidic tundra nutrient addition plots, Toolik Field Station, Alaska, sampled July 2011.
Soil aggregate size distribution, aggregate carbon and nitrogen, and light fraction carbon were determined for mineral soils in moist acidic tundra. Soil was sampled in control, and N+P plots of the Arctic LTER Moist Acidic Tundra plots established in 1989 and 2006.
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
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.