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27 results for “Ecosystem degradation”
Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1
<p>The purpose of Figure SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.</p>
Occurrence of blood feeding terrestrial leeches in a degraded forest ecosystem
<b>Description: </b><p>This dataset includes the abundance of two species of terrestrial leech collected at multiple sites at the SAFE project in Sabah, Malaysia. Leech collections took place over two seasons, one in the dry season of 2015 and one in the wet season of 2016. For each of the sites, four repeated visits took place and 20 minute searches were conducted within the boundaries of 25 m2 vegetation plots. As these sites have been subjected to differennt degrees of current and historic degradation, the vegetation structure data is also included for each site. For a subset of the leech sites there is corresponding mammal detection data from camera traps across the landscape, which is also included in this dataset.</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/10"><b>The effects of rainforest fragmentation on mammal community assemblages using leech blood-meal analysis</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant , NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3476542">here</a></p><p><b>Files: </b>This consists of 1 file: Drinkwater2019_leech_occurrence.v2.xlsx</p><p><b>Drinkwater2019_leech_occurrence.v2.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Leech abundance and survey-covariates 2015</b> (described in worksheet abundance2015)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2015 between February and June. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Leech abundance and survey-covariates 2016</b> (described in worksheet abundance2016)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2016 between September and December. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Site specific covariates</b> (described in worksheet covariates)</p><p>Description: Vegetation structure data associated with each site for which leech surveys were conducted. The metrics include canopy height, moran's I and plant-area-index. These data were extracted from LiDAR data with a 50 m2 buffer around the centroid for each site.</p><p>Number of fields: 6</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point code (Field type: location)</li><li><b>tch</b>: Top of canopy height per site (Field type: numeric)</li><li><b>canopy_height_moran</b>: Habitat heterogeneity - Morans I - per site (Field type: numeric)</li><li><b>canopy_height_sd</b>: Standard deviation of canopy height (Field type: numeric)</li><li><b>pai_mean</b>: Mean plant area index at site (Field type: numeric)</li><li><b>pai_sd</b>: Plant area index standard deviation (Field type: numeric)</li></ul></li><li><p><b>Mammal detections </b> (described in worksheet mammals)</p><p>Description: This dataset contains the mammal detections recorded from camera traps at a subset of the leech survey locations. Sampling effort is also included as a measure of survey effort. </p><p>Number of fields: 27</p><p>Number of data rows: 83</p><p>Fields: </p><ul><li><b>Camera</b>: Name of camera (Field type: location)</li><li><b>CTNs</b>: Measure of trapping effort - number of nights the cameras were operational (Field type: numeric)</li><li><b>Asian Elephant</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banded Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banteng</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bearded Pig</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bornean Yellow Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Common Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Greater Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Leopard Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Lesser Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Marbled Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Masked Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Moonrat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Mousedeer sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Muntjac sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Orangutan</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Pig-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Red Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sambar Deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sun Bear</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sunda Pangolin</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Thick-spined Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-02-01 to 2016-12-31</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> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  Annelida <br> -  -  -  Clitellata <br> -  -  -  -  Arhynchobdellida <br> -  -  -  -  -  Haemadipsidae <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  -  <i>Haemadipsa picta</i> <br></div><p></p>
Data from: Cyanobacterial colonization on epilithic mosses in degraded karst ecosystem: The role of moss traits and environmental factors
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Invasive crayfish: drivers or passengers of degradation in freshwater ecosystems?
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Data from: Volcanic soils alleviate the allelopathic capacity of Empetrum nigrum in degraded tundra ecosystems
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FIGURE 2 in A new species of Sphaerotheca Günther, 1859 (Anura: Dicroglossidae) from the degraded urban ecosystems of Bengaluru, Deccan Plateau, India
FIGURE 2. Maximum Likelihood (ML) gene tree for species of Sphaerotheca based on 585 bp of mitochondrial 16S rRNA gene.
FIGURE 4 in A new species of Sphaerotheca Günther, 1859 (Anura: Dicroglossidae) from the degraded urban ecosystems of Bengaluru, Deccan Plateau, India
FIGURE 4. Sphaerotheca bengaluru sp. nov. holotype in preservative (A) Dorsal view (B) Ventral view.
FIGURE 5 in A new species of Sphaerotheca Günther, 1859 (Anura: Dicroglossidae) from the degraded urban ecosystems of Bengaluru, Deccan Plateau, India
FIGURE 5. Habitat of Sphaerotheca bengaluru sp. nov. from the type locality, a degraded ecosystem of Bengaluru.
Data from: Effects of small-scale, shading-induced seagrass loss on blue carbon storage: Implications for management of degraded seagrass ecosystems
1. Seagrass meadows are important global 'blue carbon' sinks. Despite a 30% loss of seagrasses globally during the last century, there is limited empirical research investigating the effects of disturbance and loss of seagrass on blue carbon stocks. 2. In this study, we hypothesised that seagrass loss would reduce blue carbon stocks. Using shading cloth, we simulated small-scale die-offs of two subtropical seagrass species, Halodule wrightii and Thalassia testudinum, in a dynamic northern Gulf of Mexico lagoon. The change in quantity and quality of sediment organic matter and organic carbon were compared among kill, control and bare plots before the kill treatment, shortly after the kill treatment and 11 months after the kill treatment. 210 Pb age dating was performed on bare and Thalassia plots at 11 months to evaluate the impact of sediment erosion in the absence of vegetation. 3. The small-scale die-off led to a 50-65% organic matter (OM) loss in the sediment in the top 8 cm of Halodule plots. Thalassia plots lost significant portions OM (50%) and organic carbon (C; 21-47%) in only the top 1 cm of sediment. The 210 Pb profiles indicated Thalassia die-off reduced the C sequestration rate by 10%, in addition to a loss of ~1 years' worth of C stocks (~22 g m-2<). Furthermore, analyses on O Morg quality indicated a loss of labile OM/C and enhanced remineralisation by microbes. 4. Synthesis and applications: This study provides empirical evidence that small-scale shading-induced seagrass die-offs can reduce seagrass carbon sequestration capacity and trigger losses of blue carbon stocks. While the losses recorded here are modest, these losses in blue carbon storage capacity are notable due to the proximity of shading structures (for example, boat docks) to seagrass habitats. Thus, policies to avoid or protect seagrass habitats from common small-scale, shading disturbances are important for optimising both carbon sequestration capacity and coastline development and management.17-Nov-2017
Data from: Effects of small-scale, shading-induced seagrass loss on blue carbon storage: Implications for management of degraded seagrass ecosystems
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Figure 1 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 1 Map of Peninsular Malaysia (left) showing the location of Bidong Island, off the Terengganu coast, indicated by the red square. Map of Bidong Island (right) with the study locations indicated by red circles.
FIGURE 3 in A new species of Sphaerotheca Günther, 1859 (Anura: Dicroglossidae) from the degraded urban ecosystems of Bengaluru, Deccan Plateau, India
FIGURE 3. (A) Sphaerotheca bengaluru sp. nov. and (B) S. breviceps in life from the type locality.
FIGURE 1 in A new species of Sphaerotheca Günther, 1859 (Anura: Dicroglossidae) from the degraded urban ecosystems of Bengaluru, Deccan Plateau, India
FIGURE 1. Map showing type localities of extant species of Sphaerotheca in South Asia.
Data from: Resilience of peatland ecosystem services over millennial timescales: evidence from a degraded British bog
1. Many peatland ecosystems in Europe have become degraded in the last century owing to the effects of drainage, burning, pollution, and climate change. There is a need to understand the drivers of peatland degradation because management and restoration interventions can affect the natural ecohydrological dynamics of such sensitive environments, and are expensive. However, if given enough time peatlands may have the ability to recover spontaneously without deliberate action. 2. We use a detailed multiproxy palaeoecological dataset from a degraded raised bog in Northern England to examine its ecosystem stability and long-term dynamics in response to anthropogenic disturbance over a variety of timescales. One feature of many degraded peatlands (including our study site) is the local dominance of Molinia caerulea (purple moor-grass), which has expanded at the expense of characteristic peatland plants, including sedges and Sphagnum mosses. 3. Our data show that there has been a long history of human impacts at the site which have culminated in its current unfavourable condition. Several distinct episodes of past peat cutting are evident as hiatuses in peat accumulation; however, peat accumulation and plant community structure have subsequently recovered spontaneously. The appearance of M. caerulea occurs coevally with an unprecedented variety of recent anthropogenic impacts, all of which have arguably contributed to providing a suitable environment for its rise to dominance. We have dated the appearance of M. caerulea to the latter half of the twentieth century which corresponds to a number of anthropogenic press disturbances, including: dust loading from post-war expansion of the adjacent quarry; burning; drainage; airborne pollution; and contamination from soil dust and agrochemicals. 4. Synthesis Our study demonstrates the importance of palaeoecology for understanding the trajectories of peatland development and ecosystem dynamics, including their resilience and resistance to pulse and press disturbances. We show that peatlands have the capability to recover spontaneously from severe disturbances such as peat cutting, albeit on timescales much longer than those applied to monitoring of restoration efforts. The implications are relevant for determining whether it is better to manage and restore peatlands, or to allow them to recover naturally without human intervention.
Data from: Resilience of peatland ecosystem services over millennial timescales: evidence from a degraded British bog
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Figure 9 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 9 Lepidodactylus lugubris
Figure 8 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 8 Gekko gecko
Figure 2 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 2 Kaloula pulchra
Figure 7 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 7 Gekko cicakterbang
Figure 6 from: Fatihah-Syafiq M, Badli-Sham BH, Fahmi-Ahmad M, Aqmal-Naser M, Rizal SA, Azmi MSA, Grismer LL, Ahmad AB (2020) Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162. https://doi.org/10.3897/zookeys.985.54737
Figure 6 Cnemaspis bidongensis
ScienceDex guides
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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.