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BAM Generalized National Models Documentation, Version 4.0
<h2>A generalized modeling framework for spatially extensive species abundance prediction and population estimation</h2> <p><span>In the face of rapid environmental change, spatially explicit estimates of species abundance and distribution are needed to inform conservation planning and management decisions across a range of spatial scales. We present a generalized modeling framework bridging the gap between local studies and regional to national management needs by compiling and harmonizing data from many sources to predict avian abundance at a fine resolution and broad extent. We first applied detectability offsets to integrate avian point-count data from a large collection of research and monitoring projects across the entire breadth of subarctic Canada (>250,000 unique sampling locations). We then subsampled the data by two time periods and sixteen geographic regions and developed boosted regression trees to model the density of 143 boreal landbird species as a function of environmental covariates representing climate, local- (250 m) and landscape-level (up to ~1.5 km) vegetation composition, land cover, and topography. Finally, bootstrapped model predictions for each region were combined to generate predictive density maps, habitat- and region-specific density estimates, and Canada-wide population estimates. Our models estimated a total of approximately 3.56 billion breeding males (7.13 billion individuals) across subarctic Canada, with the majority breeding in boreal and hemi-boreal regions. Forest generalist species made up nearly half of this estimate (1.57 billion breeding males), followed by boreal forest specialist species (1.05 billion), habitat generalists (350 million), and species associated with eastern forests (274 million), grasslands (124 million), western forests (74.7 million), wetlands (63.5 million), and Arctic tundra (17.7 million). Introduced species comprised 48.9 million breeding males. An analysis of variable importance showed that, across species, most of the variation in bird abundance was explained by landscape-level vegetation composition, suggesting that the effect of climate on bird abundance is mostly indirect, via vegetation, but that landscape-level variables are needed to capture this variation. Model classification accuracy was highest from a habitat perspective for forest- and grassland-associated species (lowest for mountain- and urban-associated species); and for Regulidae and Phasianidae from a taxonomic perspective (lowest for Bombycillidae and Paridae). In developing these models, we created a standardized, updatable, and reproducible workflow that can be used to update these analytical products and improve their utility for conservation and management planning.</span></p> <p>This data set contains:</p> <ul> <li>Reproducible code for the modeling approach based on <https://github.com/borealbirds/LandbirdModelsV4></li> <li>Source code for the website at <https://borealbirds.github.io/> based on <https://github.com/borealbirds/borealbirds.github.io></li> <li>Data and image assets for the website based on <https://github.com/borealbirds/api></li> </ul> <p>Please note, in late March 2025, we discovered a systematic error in the offsets used in these models, and have since updated the products to correct that error. For more information, please see the <https://github.com/borealbirds/QPAD-offsets-correction> repository for further details or email <bamp@ualberta.ca> for assistance.</p>
National Open Access Monitor Survey: Organisational Identity: Responses Dataset
<p>This dataset contains the response data from the 'National Open Access Monitor Survey: Organisational Identity which was carried out between 9th October and 9th November 2023 under the National Open Access Monitor Project: <a href="https://zenodo.org/doi/10.5281/zenodo.8420404">https://zenodo.org/doi/10.5281/zenodo.8420404</a></p><p><strong>This survey was run to:</strong></p><ul><li>compile a complete list of Irish research performing organisations (RPO) and research funding organisations (RFO) to be represented by the National Open Access Monitor.</li><li>ensure each organisation/entity is categorised correctly as an RPO or an RFO (or both), as applicable.</li><li>identify if an organisation/entity is publicly funded.</li><li>identify formally affiliated organisations/entities where <strong>all </strong>research outputs of one organisation/entity should also be included in the research outputs of another organisation/entity.</li><li>capture the persistent identifiers for RPOs, RFOs and publishers, to enable identification of relevant research outputs for the National Open Access Monitor.</li></ul><p><strong>To note: </strong></p><ul><li>Respondents' email addresses have been redacted.</li><li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor A, Research Funding Organisation A, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a>.</li><li>Respondents were notified of the limits of pseudonymisation for this survey. Due to the nature and purpose of this survey on Organisational Identity, the identity of the organisation/entity could not be pseudonymised. Participants were advised only to participate if they were happy to do so under these conditions.</li><li>Survey responses were deleted by request of certain respondents. These responses are not included in these files.</li><li>Survey respondents were enabled to update their submissions. These changes are captured in the <i>NationalOpenAccessMonitorSurvey.OrganisationalIdentity.MasterChangeFile.</i></li></ul><p><strong>There are four files within in this dataset:</strong></p><p>- <i>Results.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.README - </i>this PDF details the changes made to the raw data, as specified in the bullet points above and a description of the files within the dataset.</p><p>- <i>Results.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.Pseudonymised.101123</i> - this is the original raw data, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised and redacted.</p><p>- <i>NationalOpenAccessMonitorSurvey.OrganisationalIdentity.MasterChangeFile.101123 </i>- this is a change file, in csv format, which documents the changes which participants requested to be made to their submissions after they were received.</p><p>- R<i>esults.NationalOpenAccessMonitorSurvey.OrganisationalIdentity.Pseudonymised.Updated.101123 - </i>this is the original raw data, pseudonymised and redacted, with the requested changes implemented.</p><p><strong>Notes for data use:</strong></p><ul><li>The "affiliated organisations/entities" section of the survey was insufficiently described in the survey text. One-to-one follow-up and support was provided to clarify to respondents that questions 12 and 13 of the survey intended to identify where <strong>all </strong>research outputs of one organisation/entity should appear on that organisation's/entities' own RPO or RFO dashboard within the National Open Access Monitor, and <strong>also </strong>on the dashboard of another organisation/entity. </li><li>Survey respondents were notified in the introduction section to the "affiliated organisations/entities" section that "<strong>only where all entities agree there is a formal relationship in place</strong> that should be reflected in the Monitor will the link be implemented". Therefore, for the National Open Access Monitor project, only where both parties have asserted that <strong>all </strong>research outputs of one organisation/entity should also appear on the dashboard of another organisation/entity, will it be considered validated and implemented. </li><li>The geographic scope of the National Open Access Monitor Project is the Republic of Ireland. Where respondent's RPO or RFO organisations/entities are based outside the Republic of Ireland, or where respondents stated affiliations with overseas organisations/entities, they will not be actioned or implemented. To note: the survey invited responses from international <i>publishers</i> to enable filtering functionality by publisher within the National Open Access Monitor, these responses are <i>not </i>out of scope. </li></ul><p>-----------------------</p><p>The context for the survey is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a>, the National Open Access Monitor Advisory Group Meeting Minutes, 18th September 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.8405472">https://zenodo.org/doi/10.5281/zenodo.8405472</a> and the Developing the National Open Access Monitor, Ireland: Stakeholder Webinar, 22nd September 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.8370578">https://zenodo.org/doi/10.5281/zenodo.8370578.</a></p><p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/ </a><a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
National Open Access Monitor, Draft Report: Stakeholder Feedback: Response Dataset
<p>This dataset contains the response data from the 'National Open Access Monitor, Draft Report: Stakeholder Feedback' Form which was open from 16th to 30th November 2023 under the National Open Access Monitor Project. A PDF reference copy of the Feedback Form is available here: <a href="https://zenodo.org/doi/10.5281/zenodo.10141988">https://zenodo.org/doi/10.5281/zenodo.10141988</a></p><p>The purpose of the form was to capture stakeholder feedback on the National Open Access Monitor, Ireland Draft Report, for actioning by OpenAIRE in the final National Open Access Monitor Report to be delivered in January 2024. The draft is an interim report, and includes reference to the initial feedback from IReL and the National Open Access Monitor Project Advisory Group.</p><p><strong>To note: </strong></p><ul><li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor I, Research Performing Organisation I, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a></li><li>This is the original raw data file, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised.</li></ul><p>-----------------------</p><p>The context for the feedback form is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a>, the National Open Access Monitor Advisory Group Meeting Minutes, 27th October 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.10105023 ">https://zenodo.org/doi/10.5281/zenodo.10105023 </a>and the OpenAIRE National Open Access Monitor Ireland, Draft Report: <a href="https://zenodo.org/doi/10.5281/zenodo.10136295">https://zenodo.org/doi/10.5281/zenodo.10136295</a></p><p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/ </a><a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
Conductivity–Temperature–Depth (CTD) and dissolved oxygen profile data from shipboard surveys collected within Olympic Coast National Marine Sanctuary, 2005-2023
<p>This data set includes Conductivity-Temperature-Depth (CTD) and dissolved oxygen profile data that were collected along Washington State’s outer coast within Olympic Coast National Marine Sanctuary towards the northernmost extent of the California Current System. Measurements were made at fourteen hydrographic stations during mooring deployment, recovery, and maintenance cruises between the months of May and October from 2005–2023. The 792 CTD profiles were acquired using Sea-Bird Scientific 19 SeaCAT or 19plus SeaCAT CTD profilers with associated SBE-43 (Sea-Bird Electronics) or Beckman or YSI-type (Yellow Springs Instruments) dissolved oxygen sensors. The data were processed via Sea-Bird Scientific’s SBE Data Processing application using six of the modules in the following order: <em>Data Conversion, Filter, Align CTD, Loop Edit, Derive, and Bin Average</em>. These processing steps and associated methods are the same as those used to process CTD data that make up the <a href="../records/5814071">Newport Hydrographic Line time series</a> located off the central Oregon coast thus allowing for a direct comparison between the two regions.</p> <table> <tbody> <tr> <td><strong>Station Name </strong></td> <td><strong>Latitude</strong></td> <td><strong>Longitude</strong></td> <td><strong>Water Depth (m, MLLW)</strong></td> </tr> <tr> <td><strong>Makah Bay (MB)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>MB015</td> <td>48.3254oN</td> <td>124.6768oW</td> <td>15</td> </tr> <tr> <td>MB042</td> <td>48.3240oN</td> <td>124.7354oW</td> <td>42</td> </tr> <tr> <td><strong>Cape Alava (CA)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CA015</td> <td>48.1663oN</td> <td>124.7568oW</td> <td>15</td> </tr> <tr> <td>CA042</td> <td>48.1660oN</td> <td>124.8234oW</td> <td>42</td> </tr> <tr> <td>CA065 </td> <td>48.1659oN</td> <td>124.8949oW</td> <td>65</td> </tr> <tr> <td><strong>Teahwhit Head (TH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>TH015</td> <td>47.8761oN</td> <td>124.6195oW</td> <td>15</td> </tr> <tr> <td>TH042</td> <td>47.8762oN</td> <td>124.7334oW</td> <td>42</td> </tr> <tr> <td>TH065 </td> <td>47.8767oN</td> <td>124.7967oW</td> <td>65</td> </tr> <tr> <td><strong>Kalaloch (KL)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>KL015</td> <td>47.6008oN</td> <td>124.4284oW</td> <td>15</td> </tr> <tr> <td>KL027</td> <td>47.5946oN</td> <td>124.4971oW</td> <td>27</td> </tr> <tr> <td>KL050 </td> <td>47.5933oN</td> <td>124.6112oW</td> <td>50</td> </tr> <tr> <td><strong>Cape Elizabeth (CE)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CE015</td> <td>47.3568oN</td> <td>124.3481oW</td> <td>15</td> </tr> <tr> <td>CE042</td> <td>47.3531oN</td> <td>124.4887oW</td> <td>42</td> </tr> <tr> <td>CE065 </td> <td> 47.3528oN</td> <td>124.5669oW</td> <td>65</td> </tr> </tbody> </table>
National price indices of materials and labor in Spain
<p>The national materials and labor price index in Spain provides a comprehensive measure of the costs associated with construction and industry in the country. This unique index reflects fluctuations in the prices of a wide range of materials, such as glass, chemicals, wood, aluminum, copper, steel materials, plastic products, spotlights and luminaires, ceramics and others, as well as the labor costs associated with the workforce in these sectors. For each material we have a month-by-month index starting from 2012 until a couple of months in 2023, the data until 2021 were validated by the INE<a href="https://www.ine.es/index.htm">(Instituto Nacional de Estadística)</a></p>
Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals
<p>This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.</p>
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Curated Estonian National Bibliography - persons
<p>This curated dataset is derived from the persons authority file of the Estonian National Bibliography (ENB), a comprehensive catalog of publications written in Estonian, published in Estonia, or focusing on Estonian culture and people. Designed for computational analysis, this dataset adapts the original authority file for research and cultural exploration. Through a systematic process of filtering, cleaning, and harmonizing, the ENB dataset is presented in a streamlined tabular format that retains rich metadata while improving accessibility. Fields selected for inclusion are harmonized and, where possible, linked to external sources, offering an optimized and reproducible resource for historical, cultural, and bibliographic research.</p>
Curated Estonian National Bibliography - books
<p>This curated dataset is derived from the books subset of the Estonian National Bibliography (ENB), a comprehensive catalog of publications written in Estonian, published in Estonia, or focusing on Estonian culture and people. Designed for computational analysis, this dataset adapts the original catalog for research and cultural exploration.</p> <p>Through a systematic process of filtering, cleaning, and harmonizing, the ENB dataset is presented in a streamlined tabular format that retains rich metadata while improving accessibility. Fields selected for inclusion are harmonized and, where possible, linked to external sources, offering an optimized and reproducible resource for historical, cultural, and bibliographic research.</p>
National Checklists 2017: Martinique Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Martinique collected using effechecka and geonames polygons
Reactive nitrogen fluxes over peatland (Bourtanger Moor) and forest (Bavarian Forest National Park) using micrometeorological measurement techniques
<p>Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere-atmosphere exchange of reactive nitrogen compounds. We applied novel fast-response instruments in eddy-covariance setups for continuous determination of surface ammonia (NH<sub>3</sub>) and total reactive nitrogen (<span class="math-tex">\(\Sigma\)</span>N<sub>r</sub>) fluxes using two different analytical devices. While high-frequency measurements of ammonia were measured with a quantum cascade laser absorption spectrometer (QCL), a custom-built converter called TRANC coupled to a chemiluminescence detector was used for the determination of total reactive nitrogen. High-resolution data of surface-atmosphere fluxes of reactive compounds are still scarce, but highly desired for testing and validating local inferential and larger scale models. We provide access to campaign data including concentrations, fluxes and ancillary measurements of meteorological data. Campaigns were conducted in natural (forest) and semi-natural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface-atmosphere exchange in different ecosystems, thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen.</p>
Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"
<p>These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>.</p> <p><strong>Abstract:</strong> </p> <p>The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices.</p> <p><strong>Code</strong></p> <p>The code to collect and process the data used in this paper is available on GitHub at <a href="https://github.com/DigitalDataLab/ASN16-18_CitationNetwork">https://github.com/DigitalDataLab/ASN16-18_CitationNetwork</a><strong>.</strong> </p> <p><strong>Dataset description</strong></p> <p>The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively.</p> <p>The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively.</p> <p>The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level.</p> <p>The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level.</p> <p>The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files.</p> <p>The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines.</p> <p>The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices. </p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
SSHOC - National Gallery - Raphael Research Resource CIDOC CRM Mapped Dataset
<p>In 2007 the <a href="https://cima.ng-london.org.uk/documentation">Raphael Research Resource</a> project began to examine how complex conservation, scientific and art historical research could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images and text, along with how the data could be stored in relation to an event driven ontology in the form of <a href="http://www.w3.org/TR/rdf-concepts/">RDF triples</a>. The original <a href="https://cima.ng-london.org.uk/documentation">main user interface</a> is still live, In 2021/21 as part of the <a href="https://www.sshopencloud.eu/">SSHOC Project</a> the raw data stored within the system was mapped to the <a href="https://www.cidoc-crm.org/">CIDOC CRM</a> using a custom set of Python scripts (<a href="https://doi.org/10.5281/zenodo.6461654">https://doi.org/10.5281/zenodo.6461654</a>). The SSHOC work aimed to make this data more <a href="https://www.go-fair.org/fair-principles/">FAIR</a> so in addition to mapping it to a standard ontology, to increase Interoperability, it has also been made available in the form of <a href="http://en.wikipedia.org/wiki/Linked_Data">open linkable data</a> combined with a <a href="http://en.wikipedia.org/wiki/SPARQL">SPARQL</a> end-point. This live data presentation can been found <a href="https://rdf.ng-london.org.uk/sshoc/">Here</a>.</p> <p>This deposit contains the CIDOC-CRM mapped data formatted in XML and an example model diagram representing some of the key relationships covered in the data-set.</p>
Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Orangutan habitat survey in Sebangau National Park, Central Kalimantan, Indonesia
<p>This dataset is used to initialise BORNEO (arBOReal aNimal movEment mOdel), as a part of publication entitled:</p> <p>Assessing the impact of forest structure disturbances on the arboreal movement oforangutans - an agent-based modelling approach.</p> <p>The article manuscript is being prepared to be submitted to Frontiers in Ecology and Evolution</p> <p><strong>Data collection</strong></p> <p>The data is collected in Sebangau, Central Kalimantan, Indonesia. Two 1-ha plots were established, each in unburned and burned forest. </p>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Nutritional table to estimate the availability of nutrients in households from the Mexican National Survey of Household Income and Expenditures (ENIGH) 2008-2020
<p>The database contains the amount of six nutrients (calories, proteins, vitamin A and C, iron, and zinc) per 100 grams/mililiters for each of the food categories used in the Mexican National Survey of Household Income and Expenditures 2008-2020.</p>
ScienceDex guides
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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.