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67 results for “community resilience”
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
SSH CENTRE - Mini-reports : Focus groups on "Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030"
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality. </p><p>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.</p><p>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.</p><p>The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe. </p><p>Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes. </p>
Dataset of Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities
<p>Data used in the article: </p> <p><strong>Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities</strong></p> <p><strong>Abstract</strong></p> <p>1- Climate models forecast changes in the amounts and distribution of rain, which may affect ecosystems worldwide, especially in drylands where water is already the limiting factor for plant life. Annual plant communities are common in drylands where they can complete their entire life cycle during the rainy period while avoiding the dry season. Moreover, seed dormancy allows them to disperse over time by remaining in the seed bank for long periods. However, the extent to which these communities will be able to tolerate increasing drought is uncertain.</p> <p>2- We performed a five-year rainfall reduction treatment under field conditions and determined its effects on annual plant communities in a Mediterranean gypsum ecosystem. We assessed the taxonomic, functional, and phylogenetic diversity of these communities each year for five years.</p> <p>3-The taxonomic and functional diversity decreased under the rainfall reduction treatment whereas the phylogenetic diversity increased. Moreover, the relative importance of species with drought-resistant functional designs increased in the community assemblages. However, after a rainy season with above average rainfall, all of the diversity values recovered completely even under the rainfall reduction treatment.</p> <p>4- Our results provide important insights into the responses of these plant communities under a climate change scenario, where they indicate high losses of diversity during drought events but rapid recovery in milder years.</p> <p><em>Synthesis</em> Our findings highlight the great resilience of annual plant communities in drylands, which may allow them to tolerate increased drought under the present climate change scenario.</p>
Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.
<p>Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.</p>
Data used in: Assessing the resilience and viability of communities in a changing environment
<p>In order to assess community resilience, we propose to analyse how variation in overall abundance of individuals affect the number of species. We define community senstivity as the ratio between rate of change in log expected number of individuals in the community and the rate of change in the log expected number of species. Second, we define community resistance as the proportional reduction in expected number of individuals that the community can sustain before expecting to lose one species. To illustrate these concepts we use four datasets of bird communities in European deciduous forests.</p> <p>We estimated the total variance of the species abundance distribution in order to calculate the community sensitivity and resistance. We found large differences in species heterogeneity and species-specific response to environmental fluctuations, the two major components of the total variance.</p> <p>The datasets are collected from various previously published sources, see references below and in the paper. The code for importing and analysing the raw data as done in the paper can be found here:<span class="ng-binding"> <a href="https://doi.org/10.5281/zenodo.8252471">https://doi.org/10.5281/zenodo.8252471</a>, but we also provide better-structured datasets for easier applications to others.<br></span></p>
Data from: Resilience metrics are robust across data qualities but sensitive to community size models
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Data used in: Assessing the resilience and viability of communities in a changing environment
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Data from: Trait networks reveal turnover in Caribbean corals and changes in community resilience through the Cenozoic
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Data from: navigating uncertainty: managing herbivore communities enhances savanna ecosystem resilience under climate change
<p>Savannas are characterized by water scarcity and degradation, making them highly vulnerable to increased uncertainties in water availability resulting from climate change. This poses a significant threat to ecosystem services and rural livelihoods that depend on them. In addition, the lack of consensus among climate models on precipitation change makes it difficult for land managers to plan for the future. Therefore, savanna rangeland management needs to develop strategies that can sustain savanna resilience and avoid tipping points under an uncertain future climate. Our study aims to analyze the impacts of climate change and rangeland management on degradation in savanna ecosystems of southern Africa, providing insights for the management of semi-arid savannas under uncertain conditions worldwide. To achieve this, we simulated the effects of projected changes in temperature and precipitation, as predicted by ten global climate models, on water resources and vegetation (cover, functional diversity, tipping points (transition from grass-dominated to shrub-dominated vegetation)). We simulated three different rangeland management options (herbivore community dominated by grazers, by browser and by mixed-feeders), each with low and high animal densities using the ecohydrological model EcoHyD. Our results identified intensive grazing as the primary contributor to the increased risk of degradation in response to changing climatic conditions across all climate change scenarios. This degradation encompassed a reduction in available water for plant growth within the context of predicted climate change. It also entails a decline in the overall vegetation cover, the loss of functionally important plant species, and the inefficient utilization of available water resources, leading to earlier tipping points. Our findings underscore that in the face of climate uncertainty, farmers' most effective strategy for securing their livelihoods and ecosystem stability is to integrate browsers and apply management of mixed herbivore communities. This management approach not only significantly delays or averts tipping points but also maintained greater plant functional diversity, fostering a more robust and resilient ecosystem that acts as a vital buffer against adverse climatic conditions.</p>
Data and Code for "No general support of functional diversity enhancing resilience across terrestrial plant communities"
<p>The data and code provided here is to support the study "No general support of functional diversity enhancing resilience across terrestrial plant communities" </p> <p>This repository contains the following files:</p> <p> The code to reproduce main analysisi and graphs in R and HTML format</p> <ul> <li>RcodeNoGeneralSupport.R</li> <li>RcodeNoGeneralSupport.html</li> </ul> <p>The data to be used for the different analyses</p> <ul> <li>ResilienceFDIndices.csv</li> <li>ResilienceFDIndices-BiomassH.csv</li> <li>ResilienceFDIndices-BiomassW.csv</li> <li>ResilienceFDIndices-CompositionW.csv</li> <li>ResilienceFDIndices-Herbaceous.csv</li> <li>ResilienceFDIndices-woody.csv</li> <li>ResilienceSR.csv</li> <li>ResilienceSR-BiomassH.csv</li> <li>ResilienceSR-BiomassW.csv</li> <li>ResilienceSR-CompositionW.csv</li> <li>ResilienceSR-herbaceous.csv</li> <li>ResilienceSR-woody.csv</li> </ul> <p>The detailed information for each variable in each data set</p> <ul> <li>README.txt</li> </ul>
Investigating the resilience of termite communities to logging and climate change in Borneo
<b>Description: </b><p>This project set out to quantify the tolerance of termite communities to climate change, or more specifically, temperature and humidity change, two climatic variables that have been hypothesised to drive species distributions (particularly for small ectotherms such as termites). The data presented here are the tolerances of termites to increasing temperatures, and decreasing humidities. <br><br>The thermal data was recorded by inserting termites into individual glass vials, placing those sealed vials into a water bath, and increasing the temperature until they could no longer function. This temperature was recorded, and taken as CTmax (Critical Thermal Maximum), for each individual termite. These data can be found in the TemperatureData worksheet. <br><br>The humidity data was recorded slightly differently. Groups of termites (of the same genus) were weighed and placed in one of two types of glass vial. Dessicated vials also contained silica gel (and a barrier to prevent termite interaction with the gel) which reduced the humidity to an average of 30%. Control vials did not contain any silica gel and had an average humidity of 85%. These vials were removed at one of 5 time points, and the termites were weighed again, and weight change was recorded. This weight change was attributed to water loss. <br><br>The body water data was used to calculate the proportion of body mass that was water, for multiple termite genera. This was done so that percentage of body water lost could be calculated for the humidity experiment, rather than an absolute value of water loss (as termites vary in size, using absolute values would cause false conclusions). </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/31"><b>Investigating the resilience of termite communities to logging and climate change in Borneo</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=33">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Thermal tolerance data</b> (Worksheet TemperatureData)</p><p>Dimensions: 1256 rows by 17 columns</p><p>Description: Thermal tolerance data of termites</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that each termite was taken from (Field type: ID)</li><li><b>Day</b>: The day on which the experiment took place (Field type: ID)</li><li><b>Termite_no</b>: The unique termite number, missing numbers are due to non-experimental deaths (Field type: ID)</li><li><b>Experiment</b>: Whether it was the first or second experiment from the same colony (Field type: Replicate)</li><li><b>Family</b>: The family of the termite (Field type: ID)</li><li><b>Genus</b>: The genus of the termite (Field type: ID)</li><li><b>Species</b>: The species (where known) of the termite (Field type: ID)</li><li><b>Name</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>CTmax</b>: The critical thermal maximum of the termite, or the temperature that it died at (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Nest_type</b>: The type of nest that the termite builds (Field type: Categorical Trait)</li><li><b>Nest_Layer</b>: The layer within the forest that the nest is built (Field type: Categorical Trait)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Humidity tolerance data</b> (Worksheet HumidityData)</p><p>Dimensions: 169 rows by 16 columns</p><p>Description: Humidity tolerance data of 4 termite genera</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tube</b>: The unique tube number that the termites were placed in (Field type: ID)</li><li><b>Taxa</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Time</b>: The five time points that the tubes were removed at (Field type: Numeric)</li><li><b>Treatment</b>: Whether the termites were placed in a control or desiccated tube (Field type: Categorical)</li><li><b>Initial</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Finish</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage_lost</b>: Proportion of body mass change (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>No_termites</b>: Number of termites placed in the tube (Field type: Numeric)</li><li><b>No_dead</b>: Number of termites that were dead at the point of the second weighing (Field type: Numeric)</li><li><b>Percentage_Dead</b>: Percentage of termites that are dead at point of second weighing (Field type: Numeric)</li></ul><br></li><li><p><b>Termite total body water data</b> (Worksheet BodyWaterData)</p><p>Dimensions: 38 rows by 12 columns</p><p>Description: Data calculating the total body water of 4 termite genera, this data was used in the humidity data to calculate the percentage of body water lost during the experiment</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tray_no</b>: The unique tray number that the termites were placed in (Field type: ID)</li><li><b>Genus</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Weight_start</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Weight_end</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage</b>: Percentage of body mass that is water (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-01 to 2016-07-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  -  - [<i>Homallotermes foraminifer</i>]<br> -  -  -  - Kalotermitidae<br> -  -  -  -  - <i>Glyptotermes</i><br> -  -  -  -  -  - [Glyptotermes sp.]<br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Coptotermes</i><br> -  -  -  -  -  - [Coptotermes sp.]<br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  -  - [<i>Parrhinotermes pygmaeus</i>]<br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  -  -  - [<i>Schedorhinotermes sarawakensis</i>]<br> -  -  -  -  -  - [Schedorhinotermes sp.]<br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  -  - [Bulbitermes sp.]<br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  -  - [Dicuspiditermes sp.]<br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  -  - [<i>Globitermes globosus</i>]<br> -  -  -  -  - <i>Hospitalitermes</i><br> -  -  -  -  -  - [<i>Hospitalitermes hospitalis</i>]<br> -  -  -  -  -  - [<i>Hospitalitermes bicolour</i>]<br> -  -  -  -  - [Lacessitermes sp.]<br> -  -  -  -  - <i>Longipeditermes</i><br> -  -  -  -  -  - [Longipeditermes sp.]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  -  - [<i>Macrotermes gilvus</i>]<br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  -  - [Microcerotermes sp.]<br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  -  - [<i>Nasutitermes havilandi</i>]<br> -  -  -  -  -  - [Nasutitermes sp.]<br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  -  - [Odontotermes sp.]<br></div><p></p>
Identifying Hot Spots in Rural Community Resilience in Scotland - database
<p>Database of nearest resilience infrastructure from each Scottish postcode, commissioned and funded by the National Centre for Resilience.</p> <p>Data are in <a href="http://www.geopackage.org/">geopackage format</a>, which is curated by the open geospatial consortium. The data format can be read by GDAL, and hence all major analytical and spatial software (e.g. R, Python, QGIS). The database has two main tables:</p> <ul> <li>datazones - a spatial table of boundary polygons. Available from <a href="https://data.gov.uk/dataset/ab9f1f20-3b7f-4efa-9bd2-239acf63b540/data-zone-boundaries-2011">data.gov.uk</a> under the <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">open government licence</a>.</li> <li>postcode_to_POI - a table of postcode to POI type distances with lookups for a range of administrative boundaries.</li> </ul> <p>An example query (in R) is shown in 10.5281/zenodo.3386179 to access data in the geopackage.</p> <p>The database was built using the following datasets:</p> <ul> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-roads.html">Ordnance Survey Open Roads</a></li> <li><a href="https://www.ordnancesurvey.co.uk/business-and-government/products/code-point-open.html">Ordnance Survey Code-Point Open</a></li> <li><a href="https://digimap.edina.ac.uk/webhelp/os/data_information/os_products/points_of_interest.htm">Ordnance Survey Points of Interest</a></li> </ul> <p>The following copyright licences apply to this dataset:</p> <p>© Crown Copyright and Database Right 2019. Ordnance Survey (Digimap Licence).<br> This material includes data licensed from PointX Database Right/Copyright 2019.<br> Contains NRS data © Crown copyright and database right 2019.</p>
Trait Networks: Assessing Marine Community Resilience and Extinction Recovery
<p>Data and R code for Trait networks. From the paper:</p> <p> </p> <p><strong>Trait Networks: Assessing Marine Community Resilience and Extinction Recovery</strong></p> <p><strong>Summary</strong></p> <p><strong>Extensive global habitat degradation and the climate crisis are tipping the biosphere towards a “sixth” mass extinction and marine communities will not be spared from this catastrophic loss of biodiversity. The resilience of marine communities following large-scale disturbances or extinction events is mediated by the life-history traits of species and their interplay within communities. The presence and abundance of traits in communities provide proxies of function, but whether the breakdown of their associations with species loss can delineate functional loss remains unclear. Here, we propose that relationships between traits within trait networks provide unique perspectives on the importance of specific traits, trait combinations, and their role in supporting the stability of communities, whilst allowing quantification of the vulnerability of both past deep time and present-day marine communities.</strong></p>
Meta-data and coding for Resilience of Coastal Communities Nexus Evidence Map
<p>Data extracted and coded for studies relevant to the Evidence Map associated with the manuscript "Mapping research at the nexus of resilience, wellbeing and environmental sustainability in UK marine systems"</p>
Residents perspectives on human-wildlife conflict management to build community resilience in Chitwan National Park, Nepal
<ol> <li>Human-wildlife conflict can significantly impact economic, social, and ecological systems critical to promoting sustainable development. Effectively managing conflicts between people and wildlife that share a common landscape requires the implementation of effective management strategies that aim to reduce the impacts of HWC and promote coexistence as preferred by the community. However, many studies have often overlooked social aspects, especially the residents' perceptions of HWC management, that can help build community resilience.</li> <li>Residents and wildlife share a common landscape in the Chitwan National Park in Nepal. Competition for resources grows as the human-modified landscape provides a new form of habitat for wildlife. We used Importance-Performance Analysis (IPA) to capture the residents' (n=506) perspectives on the importance of HWC management strategies and their performance by park management to prioritize strategies that could help build community resilience.</li> <li>We found notable mean performance-importance gaps for the eight HWC management strategies, representing the park management's inability to meet the desired need of farmers and non-farmers. The IPA matrix grid shows the three strategies - skill, livelihood, and compensation - that need immediate attention from park management as they fall under high-priority strategies in quadrant II. The two-way ANOVA results revealed that the residents' perspectives on importance and performance in all management sectors differ.</li> <li>We conclusively recommend developing site-and context-specific HWC management plans that consider the affected community's livelihood needs, which are essential for increasing the operational effectiveness of HWC management. Park management should prioritize strategies in sectors more vulnerable to HWCs to secure community support for long-term conservation goals.</li> <li>This study will be a key reference for identifying context-specific management strategies that incorporate community resilience in the management of human-wildlife conflict. This could inform HWC management policy and conservation planning to achieve coexistence that benefits both people and wildlife, particularly in low-income countries with similar socio-ecological settings. Overall, our findings provide a new perspective on human-wildlife conflict management that policymakers, researchers, and protected area managers around the world can use to build community resilience to facilitate coexistence.</li> </ol>
Response and resilience of karst subterranean estuary communities to precipitation impacts
<p>Data and code used in the analyses for the article "Response and resilience of karst subterranean estuary communities to precipitation impacts" published in the journal "Ecology and Evolution" in 2023. See Metadata file for the description on each file.</p>
A Family-based, Resilience-focused Intervention for War-affected Communities in North-eastern Democratic Republic of Congo
ClinicalTrials.gov study NCT01542398. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Ecological resilience in a primate community affected by gold mining in Suriname
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Residents perspectives on human-wildlife conflict management to build community resilience in Chitwan National Park, Nepal
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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.