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1,069 results for “Data Management”
Data from: Wind turbines in managed forests partially displace common birds
<p><span>Wind turbines are increasingly being installed in forests, which can lead to land use disputes between climate mitigation efforts and nature conservation. Environmental impact assessments precede the construction of wind turbines to ensure that wind turbines are installed only in managed or degraded forests that are of potentially low value for conservation. It is unknown, nevertheless, if animals deemed of minor relevance in environmental impact assessments are affected by wind turbines in managed forests. We investigated the impact of wind turbines on common forest birds, by counting birds </span><span>along an impact-gradient of wind turbines</span><span> in 24 temperate forests in Hesse, Germany. </span><span>During 860 point counts, we counted 2,231 birds from 45 species. Bird communities were strongly related to forest structure, season and the rotor diameter of wind turbines, but were not related to wind turbine distance. For instance, bird abundance decreased in structure-poor (-38%) and monocultural (-41%) forests with wind turbines, and in young (-36%) deciduous forests with larger and more wind turbines (-24%). Overall, our findings suggest that wind turbines in managed forests partially displace common forest birds. If these birds are displaced to harsh environments, wind turbines might indirectly contribute to a decline of their populations. Yet, forest bird communities are locally more sensitive to forest quality than to wind turbine presence. To prevent further displacement of forest animals, forests of lowest quality for wildlife should be preferred in spatial planning for wind turbines, for instance small and structure-poor monocultures along highways.</span></p>
Data for: Population genomics and conservation management of the threatened black-footed tree-rat (Mesembriomys gouldii) in northern Australia
<p>Genomic diversity is a fundamental component of Earth's total biodiversity and requires explicit consideration in efforts to conserve biodiversity. To conserve genomic diversity, it is necessary to measure its spatial distribution and quantify the contribution that any intraspecific evolutionary lineages make to overall genomic diversity. Here, we describe the range-wide population genomic structure of a threatened Australian rodent, the black-footed tree-rat (<em>Mesembriomys</em> <em>gouldii</em>), aiming to provide insight into the timing and extent of population declines across a large region with a dearth of long-term monitoring data. By estimating recent trajectories in effective population sizes at four localities, we confirm widespread population decline across the species' range, but find that the population in the peri-urban area of the Darwin region has been more stable. Based on current sampling, the Melville Island population made the greatest contribution to overall allelic richness of the species, and the prioritisation analysis suggested that conservation of the Darwin and Cobourg Peninsula populations would be the most cost-effective scenario to retain more than 90% of all alleles. Our results broadly confirm current sub-specific taxonomy and provide crucial data on the spatial distribution of genomic diversity to help prioritise limited conservation resources. Along with additional sampling and genomic analysis from the far eastern and western edges of the black-footed tree-rat distribution, we suggest a range of conservation and research priorities that could help improve black-footed tree-rat population trajectories at large and fine spatial scales, including the retention and expansion of structurally complex habitat patches.</p>
Data from: Hurry Up and Wait: Managing the Inherent Mismatches in Timescales in Natural and Artificial Photosynthetic Systems
<p>Houle, Frances A.; Yano, Junko; Ager, Joel W.;<br> Hurry Up and Wait: Managing the Inherent Mismatches in Timescales in<br> Natural and Artificial Photosynthetic Systems</p> <p><strong>Files</strong><br> Supplementary data coupled reaction-transport and cascades.xlxs<br> Input parameters for Kinetiscope calculations and associated figure data</p> <p>CO2R microkinetic model-DRC.ipynb<br> CO2R microkinetic model-DRC.pdf<br> Jupyter notebook and pdf for degree of rate control in CO2 reduction<br> Packages used:<br> Python: 3.7.7<br> Numpy: 1.21.5<br> SciPy: 1.7.3<br> Matplotlib: 3.5.1</p> <p>Wang 2018 Pathway 1 only DRC.ipynb<br> Wang 2018 Pathway 1 only DRC.pdf<br> Jupyter notebook and pdf for degree of rate control photoelectrocatalytic water oxidation<br> Python: 3.7.7<br> Numpy: 1.21.5<br> SciPy: 1.7.3<br> mpmath: 1.2.1<br> Matplotlib: 3.5.1</p>
Data from: Vegetation management shapes arthropod and bird communities in an African savanna
<p>Habitat heterogeneity is a key driver of the diversity and distribution of species. African savannas are experiencing changes to their vegetation structure causing shifts towards increased woody plant cover, which results in vegetation structure homogenization. Given the impact that increasing woody plant cover has on patterns of animal use, resource managers across Africa are implementing habitat management practices that are intended to reduce woody plant cover. To understand the ecological implications of various habitat management practices on arthropod and bird communities, we leveraged large-scale tree clearing and subsequent mowing in an African savanna to understand how changes to both the herbaceous layer and woody plant cover (i.e., structural heterogeneity) may shape arthropod and bird communities at the local scale. We focused on four replicated treatments: 1) annual summer mow, 2) annual winter mow, 3) >5 years since last mow (rest), and 4) an adjacent unmanipulated savanna to act as a control. We found that the mowing treatments significantly influenced vegetation structure both with respect to tree density and herbaceous layer. Both arthropod and bird community composition varied across treatments. Grass biomass was the best predictor of arthropod richness and abundance, with arthropods selecting for areas with high biomass. Insectivorous bird richness and abundance was driven by tree density (i.e., perching locations) and not arthropod abundance. Our results suggest that vegetation management practices contribute to habitat heterogeneity at the landscape scale and increase bird species richness through species turnover. However, we caution that if a single vegetation management practice dominates the landscape, it is plausible that it could lead to the simplification of the avian community.</p>
Data on learning about green space management by upper secondary school students
<p>A workshop survey -dataset detailing how upper secondary school students learn about green space management.</p>
Guideline for a FAIR Cultural Studies Research Data Management
<p>Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“.</p> <p>Die <strong>Online-Handreichung</strong> ist verfügbar unter <a href="https://nfdi4culture.de/go/E3625">https://nfdi4culture.de/go/E3625</a>.</p> <p>Als <strong>PDF</strong> ist diese Handreichung verfügbar unter <a href="https://doi.org/10.5281/zenodo.7716941">https://doi.org/10.5281/zenodo.7716941</a>.</p>
Data Management Training Clearinghouse Metadata and Collection Statistics Report
<p>This collection contains a snapshot of the learning resource metadata from ESIP's <a href="https://dmtclearinghouse.esipfed.org">Data management Training Clearinghouse</a> (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: <a href="https://imls.gov/grants/awarded/lg-70-18-0092-18">LG-70-18-0092-18</a>) <em>Development of an Enhanced and Expanded Data Management Training Clearinghouse project.</em> The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.</p> <p>The materials included in the collection consist of the following:</p> <ul> <li><strong>esip-dev-02.edacnm.org.json.zip</strong> - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.</li> <li><strong>statistics.pdf</strong> - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, <strong>and</strong> preliminary metadata for unpublished learning resources that are not included in the shared dataset. </li> </ul> <p>The metadata fields consist of the following:</p> <table> <thead> <tr> <th scope="col">Fieldname</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>abstract_data</td> <td>A brief synopsis or abstract about the learning resource</td> </tr> <tr> <td>abstract_format</td> <td>Declaration for how the abstract description will be represented.</td> </tr> <tr> <td>access_conditions</td> <td>Conditions upon which the resource can be accessed beyond cost, e.g., login required.</td> </tr> <tr> <td>access_cost</td> <td>Yes or No choice stating whether othere is a fee for access to or use of the resource.</td> </tr> <tr> <td>accessibililty_features_name</td> <td>Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.</td> </tr> <tr> <td>accessibililty_summary</td> <td>A human-readable summary of specific accessibility features or deficiencies.</td> </tr> <tr> <td>author_names</td> <td>List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system</td> </tr> <tr> <td>author_org<br> - name<br> - name_identifier<br> - name_identifier_type</td> <td> <p><br> - Name of organization authoring the learning resource.<br> - The unique identifier for the organization authoring the resource.<br> - The identifier scheme associated with the unique identifier for the organization authoring the resource.</p> </td> </tr> <tr> <td> <p>authors<br> - givenName<br> - familyName<br> - name_identifier<br> - name_identifier_type</p> </td> <td> <p><br> - Given or first name of person(s) authoring the resource.<br> - Last or family name of person(s) authoring the resource.<br> - The unique identifier for the person(s) authoring the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.</p> </td> </tr> <tr> <td>citation</td> <td>Preferred Form of Citation.</td> </tr> <tr> <td>completion_time</td> <td>Intended Time to Complete</td> </tr> <tr> <td> <p>contact<br> - name<br> - org<br> - email</p> </td> <td> <p><br> - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.<br> - (excluded) Contact email address.</p> </td> </tr> <tr> <td>contributor_orgs<br> - name<br> - name_identifier<br> - name_identifier_type<br> - type</td> <td>- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.<br> - The unique identifier for the organization contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the organization contributing to the resource.<br> - Type of contribution to the resource made by an organization.</td> </tr> <tr> <td>contributors<br> - familyName<br> - givenName<br> - name_identifier<br> - name_identifier_type</td> <td> <p>- Last or family name of person(s) contributing to the resource.<br> - Given or first name of person(s) contributing to the resource.<br> - The unique identifier for the person(s) contributing to the resource.<br> - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.</p> </td> </tr> <tr> <td> <p>contributors.type</p> </td> <td> <p>Type of contribution to the resource made by a person.</p> </td> </tr> <tr> <td>created</td> <td>The date on which the metadata record was first saved as part of the input workflow.</td> </tr> <tr> <td>creator</td> <td>The name of the person creating the MD record for a resource.</td> </tr> <tr> <td>credential_status</td> <td>Declaration of whether a credential is offered for comopletion of the resource.</td> </tr> <tr> <td> <p>ed_frameworks<br> - name<br> - description<br> - nodes.name</p> </td> <td>- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.<br> - A description of one or more subcategories of an educational framework to which a resource is associated.<br> - The name of a subcategory of an educational framework to which a resource is associated.</td> </tr> <tr> <td>expertise_level</td> <td>The skill level targeted for the topic being taught.</td> </tr> <tr> <td>id</td> <td>Unique identifier for the MD record generated by the system in UUID format.</td> </tr> <tr> <td>keywords</td> <td>Important phrases or words used to describe the resource.</td> </tr> <tr> <td>language_primary</td> <td>Original language in which the learning resource being described is published or made available.</td> </tr> <tr> <td>languages_secondary</td> <td>Additional languages in which the resource is tranlated or made available, if any.</td> </tr> <tr> <td>license</td> <td>A license for use of that applies to the resource, typically indicated by URL.</td> </tr> <tr> <td>locator_data</td> <td>The identifier for the learning resource used as part of a citation, if available.</td> </tr> <tr> <td>locator_type</td> <td>Designation of citation locatorr type, e.g., DOI, ARK, Handle.</td> </tr> <tr> <td>lr_outcomes</td> <td>Descriptions of what knowledge, skills or abilities students should learn from the resource.</td> </tr> <tr> <td>lr_type</td> <td>A characteristic that describes the predominant type or kind of learning resource.</td> </tr> <tr> <td>media_type</td> <td>Media type of resource.</td> </tr> <tr> <td>modification_date</td> <td>System generated date and time when MD record is modified.</td> </tr> <tr> <td>notes</td> <td>MD Record Input Notes</td> </tr> <tr> <td>pub_status</td> <td>Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.</td> </tr> <tr> <td>published</td> <td>Date of first broadcast / publication.</td> </tr> <tr> <td>publisher</td> <td>The organization credited with publishing or broadcasting the resource.</td> </tr> <tr> <td>purpose</td> <td>The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.</td> </tr> <tr> <td>rating</td> <td>The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>ratings</td> <td>Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.</td> </tr> <tr> <td>resource_modification_date</td> <td>Date in which the resource has last been modified from the original published or broadcast version.</td> </tr> <tr> <td>status</td> <td>System generated publication status of the resource w/in the registry as a yes for published or no for not published.</td> </tr> <tr> <td>subject</td> <td>Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.</td> </tr> <tr> <td>submitter_email</td> <td>(excluded) Email address of person who submitted the resource.</td> </tr> <tr> <td>submitter_name</td> <td>Submission Contact Person</td> </tr> <tr> <td>target_audience</td> <td>Audience(s) for which the resource is intended.</td> </tr> <tr> <td>title</td> <td>The name of the resource.</td> </tr> <tr> <td>url</td> <td>URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.</td> </tr> <tr> <td>usage_info</td> <td>Descriptive information about using the resource, not addressed by the License information field.</td> </tr> <tr> <td>version</td> <td>The specific version of the resource, if declared.</td> </tr> </tbody> </table> <p> </p>
Code and data for: Modeling the interaction between salmon management and consumption by coastal brown bears
<p>Harvest management policy for species with strong trophic connections can reverberate through food webs and cause unintended consequences such as altering the abundance of a harvested species' predators or prey. Pacific salmon (<em>Oncorhynchus</em> spp.), a key food for many predators and an economically valuable harvested species, is generally managed for maximum sustained harvests without explicit consideration for the freshwater and terrestrial food webs which they support. The density of brown bear (<em>Ursus</em> <em>arctos</em>) populations in Alaska, USA is correlated with the amount of salmon they can access and consume, so it seems likely their populations are inadvertently affected by salmon management. We simulated the effect of salmon management policy on brown bears by customizing a general bear-salmon model using empirical data from three watersheds in southwest Kodiak, Alaska. Our goal was to quantify the effect of current salmon management policy (i.e., escapement goals and early/late run allocations) on salmon consumption by brown bears. Bears in the individually based model evaluated the value of each foraging site based on salmon abundance, salmon vulnerability, and competition with other bears and made movement decisions (among salmon spawning sites) accordingly. </p> <p>The two code files provided here contain the brown bear salmon simulation, the structure for setting and adjusting the parameters of the model, and two empirical datasets needed to run simulations.</p>
Data for: Wolverine density distribution reflects past persecution and current management in Scandinavia
<p>After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Today's spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population-level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the spatial distribution and density of these controversial species. Using spatial capture-recapture models and a data set of 742 genetically identified wolverines <em>Gulo gulo</em> collected over ½ million km<sup>2</sup> across their entire range in Norway and Sweden, we identify landscape-level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relict range along the Swedish-Norwegian border, where the wolverine population survived a long history of persecution, remains a key determinant of wolverine density today. However, regional differences in management and environmental conditions also played an important role in shaping spatial patterns in present-day wolverine density. Specifically, we found evidence of slower recolonization in areas that had set lower wolverine population goals in terms of the desired number of annual reproductions. Management of transboundary large carnivore populations at biologically relevant scales may be inhibited by administrative fragmentation. Yet, as our study shows, population-level monitoring is an achievable prerequisite for a comprehensive understanding of the distribution and density of large carnivores across an increasingly anthropogenic landscape.</p>
Data for: Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management
<p><span>Extensive applications of fishery stock enhancement worldwide bring up broad concerns about its negative effects, creating a pivotal need for science-based assessment and planning of enhancement strategies. However, the lack of mechanistic understanding of enhanced population dynamics, particularly the density-dependent processes, leads to compromise in model development and limits the capacity in predicting enhancement effects. Here, we developed an individual-based model based on dynamic energy budget theory and full life history processes, to understand the mechanism of density dependence in population dynamics that emerge from individual-level processes. We demonstrated the utility of the model framework by applying it </span><span>to an extensively enhanced species, Chinese prawn (<em>Fenneropenaeus chinensis</em></span><span>, Penaeidae</span><span>). The model could yield projections reflecting the observed trajectory of population biomass and yields. The model also delineated the key effects of density dependence on the vital rates of growth, fecundity, and starvation mortality. Regarding the manifold effects of stock enhancement, we demonstrated a dampened shape in population biomass and yields with increasing magnitude of enhancement, and trade-offs between the ecological and economic objectives, i.e., pursuing high benefit might compromise the wild population without proper management. Furthermore, we illustrated the possibility of combining stock enhancement and harvest regulation in promoting population recovery while maintaining fisheries yields. We highlight the potential of the proposed model for understanding density dependence in enhancement program, and for designing integrated management strategies. The approach developed herein may serve as a general approach to assess the population dynamics in stock enhancement and inform enhancement management.</span><span> </span></p>
Dataset of AI4ER MRes titled "Improving Urban Tree Management Using High-Resolution Satellite Data"
<p>This repository contains the data used in the Master's thesis titled "Improving Urban Tree Management Using High-Resolution Satellite Data" by Andrés C. Zúñiga-González as part of the AI4ER MRes 1st year project at the University of Cambridge.</p> <p>The folders are split into large and small training and testing datasets. These folders contain the tiles (in png and tif formats) used in the models. In addition, it includes the crowns in ESRI Shapefile format for the training and testing datasets. Finally, it contains the best model from the project (named urban_trees_Cambridge_20230630.pth).</p>
Questionnaire on the current status of Research Data Management in Mecklenburg-Vorpommern
<p>This dataset contains the questionnaires used in order to survey the current status of research data management in Mecklenburg-Vorpommern (MV). One questionnaire was used for the University of Rostock (<em>FDM_in_VREs_UR</em>) while the other was used for all other research institutes in MV. While the survey was conducted online, this dataset contains a PDF export as well as the Evasys questionnaire export which was used to conduct the online survey. Note that the PDF version does not cover the filter functionality which was used in the Evasys online survey.</p>
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
<ol> <li><span>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</span></li> <li><span>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </span></li> <li><span>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </span></li> <li><span>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</span></li> <li> <span><em>Synthesis and applications</em> – </span><span>Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.</span> </li> </ol>
Behaviour data rhino conspecific playbacks as a post-translocation management tool
<p>Raw data file for the publication "Assessing the potential of conspecific playbacks as a post-translocation management tool for white rhinoceros"</p>
GIS and Pollution Data: Designating Regional Airsheds for Air Quality Management in India
<p>Full journal article published here<br><strong>Designating Airsheds in India for Urban and Regional Air Quality Management<br></strong><a href="https://doi.org/10.3390/air2030015" target="_blank" rel="noopener">https://doi.org/10.3390/air2030015</a><strong><br></strong></p> <p>[Summary presentation <a href="https://urbanemissions.info/wp-content/uploads/docs/UEinfo-Designating-Airsheds-in-India.pptx">download</a>]</p> <p>Datasets used for proposing India's 15 regional airsheds for air quality management are the following</p> <p>PM2.5 Datasets<br>Raw data source: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a></p> <ul> <li>Gridded 0.1 degree resolution source apportionment results from WUSTL's global model simulations<br>File: india_data_pm25_wustl_source_cont_0p1deg.xlsx<br>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> <li>Gridded 0.1 degree resolution, reanalysis data from WUSTL's global model simulations<br>File: india_data_pm25_wustl_reanalysis_0p1deg.xlsx<br>Time period: 1998 to 2022, annual averages</li> <li>Gridded 0.1 degree achive for monthly averages from WUSTL's global model simulations<br>File: <a href="https://www.urbanemissions.info/wp-content/uploads/misc/IndiaSubcontinent-Gridded-Monthly-WUSTL-v4.rar">Download-44MB</a></li> </ul> <p>Population Datasets<br>Raw data source: <a href="https://landscan.ornl.gov">https://landscan.ornl.gov</a></p> <ul> <li>Gridded 0.1 degree resolution population density data<br>File: india_data_population_2021_0p1deg.xlsx</li> </ul> <p>GIS databases used in this study</p> <ul> <li>ESRI shapefile of 0.1 x 0.1 degree mesh file for the Indian Subcontinent covering longitudes from 67E to 99E and latitudes from 7N to 39N<br>File: india_gis_grids-0.1x0.1deg.rar</li> <li>ESRI shapefile of India administrative level 2 data - 28 states and 8 union territories (as of December 2023)<br>File: india_gis_states28+8_2023.rar</li> <li>ESRI shapefile of India administrative level 3 data - 755 districts (as of December 2023): district23 and states23 codes are re-designed for emissions and pollution mapping and data tracking purposes<br>File: India_gis_districts755_2023.rar (original source: <a href="https://projects.datameet.org/maps">https://projects.datameet.org/maps</a>)</li> <li>ESRI shapefile of India's Agro-Climatic zones<br>File: india_gis_agroclimatic_zones.rar (original source: <a href="https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions">https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions</a> </li> <li>ESRI shapefile of India's meteorological sub-divisions<br>File: india_gis_meteo_subdivisions.rar (original source: <a href="https://mausam.imd.gov.in/">https://mausam.imd.gov.in</a>)</li> </ul>
Code and data for: Modeling the interaction between salmon management and consumption by coastal brown bears
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Data from: A shift to metapopulation genetic management for persistence of a species threatened by fragmentation: the case of an endangered Australian freshwater fish
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Data from: Divergent population structure in five common rockfish species of puget sound, WA suggests the need for species-specific management
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Data from: Deer-mediated ecosystem service vs. disservice depends on forest management intensity
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Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
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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.