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1,630 results for “occupations”
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
<p>Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset. </p> <p>A sample of the dataset can be found here: <a href="https://doi.org/10.5281/zenodo.3457385">https://doi.org/10.5281/zenodo.3457385</a></p>
Occupancy Sensing and Activity Recognition with Cameras and Wireless Sensors
<p>This dataset contains human activity data from a wireless sensing system, which includes a Doppler motion sensor and a wireless network. The Doppler sensor is a low-cost dual Doppler sensor modified from a commercial-off-the-shelf range-controlled radar, which operates at 5.8 GHz with two directional antennas. The wireless network uses four IEEE 802.15.4 radio nodes (CC2531 from TI) to create a mesh network to measure the RSS between each pair of radio nodes operating on the 16 frequency channels at 2.4 GHz.</p> <p>For the activity experiment, we recruited human subjects to perform 42 trials of four activities (each one with two minutes duration): (1) walking in a room (10 trials), (2) sitting in a chair (10 trials), (3) lying on a bed (12 trials), and (4) body turning on a bed (10 trials). For the walking activity, the human subjects walk along different paths at different locations in the room. For the lying on bed activity, we ask human subjects to breathe normally on bed with three orientations facing upwards, right and left. Finally, for the turning on bed case, human subjects turn their bodies from one side to the other on bed with random time intervals. We also recorded two-minute data of the empty room case before and after each human subject trial. Note that each data file name has its corresponding activity in it, so it is pretty self-explanatory. </p>
Dataset: Occupancy Presence and Trajectory Dataset from an Instrumented Public Building
<p>Dataset form three PC2, 3D stereo vision camera, form Xovis.<br> It contains 5,485,350 readings, collected over 13 days, in the summer of 2019, in a public building in Denmark.<br> The monitored space is 105 m2.</p> <p>The Data description can be found on:<br> <a href="https://doi.org/10.1145/3359427.3361909">https://doi.org/10.1145/3359427.3361909</a></p> <p>The occupancy_presence_and_trajectories.csv file contains the following attributes:</p> <ul> <li>time: Time-stamp when the entries were collected [Time].</li> <li>day_id: ID for the specific day in the dataset [Number].</li> <li>workday: Was the entry collected on a workday [Boolean].</li> <li>holiday: Was the entry collected on a national holiday [Boolean].</li> <li>x: Represents the spatial position in X-axis [Number].</li> <li>y: Represents the spatial position in Y-axis [Number].</li> <li>occupant_id: Unique Occupant-ID [Number]. </li> <li>height: Occupant height in [millimeters]. </li> <li>camera_id: Name of the camera which collected the entries [String].</li> </ul> <p> </p> <p> </p>
Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students
<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>
Data from the article titled Mapping of networks and professional capital in the La Algarabía occupational center.
<p>Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/<br>AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by<br>the [grant number PRE2021-098075]</p>
Table 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 1.</b> Number of field samples (including controls) for each qPCR assay at each site sampled for eDNA in 2018 and 2019 in western Lake Erie. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. Note that samples are site-specific.</p><table><tbody><tr><th>Site</th><th>Year</th></tr><tr><th>2018</th><th>2019</th></tr><tr><th>Assay</th><th>Samples</th><th>Assay</th><th>Samples</th></tr></tbody><tbody><tr><th>DR</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>81 81 81</td></tr><tr><th>HP</th><td>GCTM10 GCTM22 GCTM32</td><td>77 77 77</td><td>GCTM10 GCTM22 GCTM32</td><td>82 82 82</td></tr><tr><th>MB</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>80 80 80</td></tr></tbody></table>
Table 4 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 4.</b> Percentage of positive eDNA replicate detections in each month and site in 2018 and 2019 in western Lake Erie (based on at least one positive detection on at least one marker and one replicate). All markers (GCTM10, GCTM 22, GCTM32) were used to calculate these proportions. Samples were collected monthly from June to November for each site. The number of telemetered grass carp detected within 7 days before sampling for eDNA is denoted in parentheses. Acoustic telemetry receivers in MB in 2018 were not available. DR = Detroit River, HP = Hot Ponds, and MB = Maumee Bay. NA denotes when acoustic telemetry receivers were not in operation.</p><table><tbody><tr><th>Year</th></tr><tr><th>Site</th><th>2018</th><th>2019</th></tr><tr><th></th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>May</th><th>June</th><th>July</th><th>August</th><th>Oct</th><th>Nov</th></tr></tbody><tbody><tr><th>DR</th><td>0.0%</td><td>2.3%</td><td>2.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>4.5%</td><td>10.6</td><td>14.1</td><td>17.4%</td><td>14.1%</td><td>2.2%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(0)</td><td>(1)</td><td>% (1)</td><td>% (1)</td><td>(2)</td><td>(2)</td><td>(0)</td></tr><tr><th>HP</th><td>0.0%</td><td>0.1%</td><td>4.1%</td><td>2.2%</td><td>15.8%</td><td>0.0%</td><td>9.0%</td><td>1.5%</td><td>34.8</td><td>1.5%</td><td>15.8%</td><td>22.7%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(NA)</td><td>(2)</td><td>(2)</td><td>% (2)</td><td>(3)</td><td>(2)</td><td>(2)</td></tr><tr><th>MB</th><td>12.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.1%</td><td>37.1</td><td>8.3%</td><td>8.3%</td><td>0.0%</td></tr><tr><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(0)</td><td>(0)</td><td>% (0)</td><td>(0)</td><td>(0)</td><td>(0)</td></tr></tbody></table>
Table 3 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 3.</b> Candidate set of hierarchical occupancy models used to estimate probability of grass carp eDNA occurrence among sites (ψ), the conditional probability of grass carp eDNA occurrence at a sampling locality within a site given that grass carp were present at the site (Θ), and the conditional probability of eDNA detection on replicate filters collected at a sampling locality given that the species is present at the sampling locality <i>(p</i>) from three sites in western Lake Erie sampled in 2018 and 2019. Covariates included location (site), time (Month) and probe type (GCTM10, GCTM22, GCTM32). Model comparison was evaluated with the Widely Applicable Information Criterion (WAIC).</p><table><tbody><tr><th>Model</th><th>WAIC</th><th>Δ WAIC</th><th>Lack of fit</th><th>Predicted Variance</th></tr></tbody><tbody><tr><th>ψ(Site)Θ(Site)p(.)</th><td>309.44</td><td>-</td><td>298.70</td><td>18.63</td></tr><tr><th>ψ(.)Θ(Site)p(.)</th><td>309.50</td><td>0.06</td><td>298.99</td><td>10.73</td></tr><tr><th>ψ(.)Θ(Month)p(.)</th><td>317.61</td><td>8.18</td><td>299.05</td><td>18.56</td></tr><tr><th>ψ(Season)Θ(.)p(.)</th><td>317.67</td><td>8.24</td><td>299.01</td><td>18.65</td></tr><tr><th>ψ(Month)Θ(.)p(.)</th><td>317.72</td><td>8.28</td><td>299.04</td><td>18.67</td></tr><tr><th>ψ(Season)Θ(Season)p(.)</th><td>317.84</td><td>8.40</td><td>299.04</td><td>18.79</td></tr><tr><th>ψ(.)Θ(Season)p(.)</th><td>317.74</td><td>8.31</td><td>299.07</td><td>18.66</td></tr><tr><th>ψ(Site)Θ(.)p(.)</th><td>317.94</td><td>8.51</td><td>299.04</td><td>18.90</td></tr><tr><th>ψ(.)Θ(.)p(.)</th><td>325.00</td><td>15.57</td><td>305.50</td><td>20.21</td></tr><tr><th>ψ(Season)Θ(Site)p(.)</th><td>325.41</td><td>15.98</td><td>305.49</td><td>19.91</td></tr><tr><th>ψ(Site + Season)Θ(.)p(.)</th><td>325.59</td><td>16.16</td><td>305.44</td><td>20.15</td></tr><tr><th>ψ(Site)Θ(Season)p(.)</th><td>325.72</td><td>16.29</td><td>305.48</td><td>20.23</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(.)</th><td>325.92</td><td>16.49</td><td>305.49</td><td>20.43</td></tr><tr><th>ψ(.)Θ(Site + Season)p(.)</th><td>326.37</td><td>16.94</td><td>305.52</td><td>20.84</td></tr><tr><th>ψ(Site)Θ(Month)p(.)</th><td>329.57</td><td>20.14</td><td>308.65</td><td>20.92</td></tr><tr><th>ψ(Month)Θ(Month)p(.)</th><td>330.14</td><td>20.71</td><td>308.66</td><td>21.84</td></tr><tr><th>ψ(Site + Season)Θ(Site)p(Probe)</th><td>377.63</td><td>68.20</td><td>276.90</td><td>100.72</td></tr><tr><th>ψ(Site + Season)Θ(.)p(Probe)</th><td>378.35</td><td>68.92</td><td>277.00</td><td>101.34</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(Probe)</th><td>378.42</td><td>68.99</td><td>277.00</td><td>101.41</td></tr><tr><th>ψ(Site + Season)Θ(Season)p(Probe)</th><td>378.55</td><td>69.12</td><td>277.09</td><td>101.46</td></tr><tr><th>ψ(Season)Θ(Site + Season)p(Probe)</th><td>379.41</td><td>69.98</td><td>277.28</td><td>102.10</td></tr><tr><th>ψ(Site)Θ(Site + Season)p(Probe)</th><td>379.62</td><td>70.19</td><td>277.40</td><td>102.21</td></tr><tr><th>ψ(.)Θ(Site + Season)p(Probe)</th><td>379.88</td><td>70.45</td><td>277.31</td><td>102.57</td></tr><tr><th>ψ(Site + Month)Θ(.)p(.)</th><td>383.56</td><td>74.13</td><td>282.86</td><td>100.69</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(.)</th><td>385.47</td><td>76.04</td><td>283.38</td><td>102.09</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(Probe)</th><td>386.95</td><td>77.52</td><td>282.90</td><td>104.05</td></tr><tr><th>ψ(.)Θ(Site + Month)p(.)</th><td>396.44</td><td>87.01</td><td>292.14</td><td>104.29</td></tr><tr><th>ψ(.)Θ(.)p(Probe)</th><td>396.45</td><td>87.01</td><td>292.14</td><td>104.29</td></tr></tbody></table>
Table 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA
<p><b>Table 2.</b> Gene region, primer, and probe sequences used to amplify GCTM10,GCTM22, and GCTM32 for grass carp.</p><table><tbody><tr><th>Gene</th><th>Primers and Probes</th><th>Sequence</th></tr></tbody><tbody><tr><th>ND2</th><td>Forward</td><td>5′- CCYTACGTACTCGCAATTCTAC -3′</td></tr><tr><th>ND2</th><td>Reverse</td><td>5′- GTGGTGGTGTTGGGCTATTA -3′</td></tr><tr><th>ND2</th><td>Probe</td><td>5′- VIC- ACCCTAACCTTTGCTAGCTCCCAC -MGBNFQ-3′</td></tr><tr><th>COII</th><td>Forward</td><td>5′- CCGACTCCTAGAAACAGATCAC -3′</td></tr><tr><th>COII</th><td>Reverse</td><td>5′- GGGACAGCTCAGGAATGTAATA -3′</td></tr><tr><th>COII</th><td>Probe</td><td>5′- 56-FAM- CCAGTTCGT/ZEN/GTCCTAGTATCTGCCGA -3IABkFQ -3′</td></tr><tr><th>COIII</th><td>Forward</td><td>5′- CCACGGACTACACGTCATTATT -3′</td></tr><tr><th>COIII</th><td>Reverse</td><td>5′-GATGTTCGGATGTAAAGTGGTATTG -3′</td></tr><tr><th>COIII</th><td>Probe</td><td>5′-NED- TTCCTAGCTGTTTGCCTTCTCCGT -MGBNFQ-3′</td></tr></tbody></table>
Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 3,125 samples and HIII05F, HIII50M, HIII95M
<p>Database and FE-models with 9,375 Honda Accord 2014 passenger occupant simulations. </p> <p> </p>
Occupant Simulation Database and FE-Model based on Honda Accord 2024 Simplified Passenger Model and SOBOL Sampling with 8,192 samples and HIII05F, HIII50M, HIII95M
<p>Database and FE-models with 24,576 Honda Accord 2014 passenger occupant simulations. </p> <p> </p>
Occupant Simulation Database and FE-Model based on Honda Accord 2024 Simplified Passenger Model and SOBOL Sampling with 256 samples and HIII05F, HIII50M, HIII95M
<p>Database and FE-models with 768 Honda Accord 2014 passenger occupant FE-simulations. </p>
Delphi method scoring for consensus statement for occupational heat safety
<p>The purpose of this consensus document was to develop feasible, evidence-based occupational heat safety recommendations to protect U.S workers that experience heat stress. Heat safety recommendations were created to protect worker health and to avoid productivity losses associated with occupational heat stress. Recommendations were tailored to be utilized by safety managers, industrial hygienists, and the employers who bear responsibility for implementing heat safety plans. An interdisciplinary roundtable comprised of 51 experts was assembled to create a narrative review summarizing current data and gaps in knowledge within eight heat safety topics: (1) heat hygiene, (2) hydration, (3) heat acclimatization, (4) environmental monitoring, (5) physiological monitoring, (6) body cooling, (7) textiles and personal protective gear, and (8) emergency action plan implementation. The consensus-based recommendations for each topic were created using the Delphi method and evaluated based on scientific evidence, feasibility and clarity. The current document presents 40 occupational heat safety recommendations across all eight topics. Establishing these recommendations will help organizations and employers create effective heat safety plans for their workplace, address factors that limit the implementation of heat safety best-practices and protect worker health and productivity.</p>
Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area
<p>This paper investigates species richness and species occupancy frequency distributions (SOFD) as well as patterns of abundance-occupancy relationship (SAOR) in Odonata (dragonflies and damselflies) in a subtropical area. A total of 82 species and 1983 individuals were noted from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa biome in southern Brazil. Odonate species occupancy ranged from 1 to 54. There were few widely distributed generalist species and several specialist species with a restricted distribution. About 70% of the species occurred in less than 10% of the water bodies, yielding a surprisingly high number of rare species, often making up the majority of the communities. No difference in species richness was found between temporal and permanent water bodies. Both temporal and permanent water bodies had odonate assemblages that fitted best with the unimodal satellite SOFD pattern. It seems that unimodal satellite SOFD pattern frequently occurred in the aquatic habitats. The SAOR pattern was positive and did not differ between permanent and temporal water bodies. Our results are consistent with a niche-based model rather than a metapopulation dynamics model.</p>
Using machine learning to model nontraditional spatial dependence in occupancy data
<p>Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.</p>
Figure 1 in Pattern of shell occupation by the hermit crab Pagurus exilis (Anomura, Paguridae) on the northern coast of São Paulo State, Brazil
Figure 1. Pagurus exilis: size frequency distribution for the individuals collected.
Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
<p>Although environmental DNA (eDNA) metabarcoding has become widely applied to gauge ecosystems in a noninvasive and cost-efficient manner, false negatives can occur due to various factors in its inherent multistage workflow. It is therefore essential to deal with this kind of species detection errors in eDNA metabarcoding to achieve accurate assessment of species distribution and diversity. To address this issue, we proposed a variant of the multispecies site occupancy model for eDNA metabarcoding studies and applied it to an eDNA metabarcoding dataset of freshwater fish communities collected in the Kasumigaura watershed in Japan.</p> <ul> </ul>
Wikidata Dump occupation-singer
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/1708">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Data from: Gobbling across landscapes: Eastern wild turkey distribution and occupancy-habitat associations
<p>Extensive restoration and translocation efforts beginning in the mid-20<sup>th</sup> century helped to reestablish eastern wild turkeys (<i>Meleagris gallopavo silvestris</i>) throughout their ancestral range. The adaptability of wild turkeys resulted in further population expansion in regions that were considered unfavorable during initial reintroductions across the northern United States. Identification and understanding of species distributions and contemporary habitat associations are important for guiding effective conservation and management strategies across different ecological landscapes. To investigate differences in wild turkey distribution across two contrasting regions, heavily forested northern Wisconsin, USA, and predominately agricultural southeast Wisconsin, we conducted 3,050 gobbling call-count surveys from March–May 2014–2018 and used multiseason correlated-replicate occupancy models to evaluate occupancy-habitat associations and distributions of wild turkeys in each study region. Detection probabilities varied widely and were influenced by sampling period, time of day, and wind speed. Spatial autocorrelation between successive stations was prevalent along survey routes but were stronger in our northern study area. In heavily forested northern Wisconsin, turkeys were more likely to occupy areas characterized by moderate availability of open land cover. Conversely, large agricultural fields decreased the likelihood of turkey occupancy in southeast Wisconsin, but occupancy probability increased as upland hardwood forest cover became more aggregated on the landscape. Turkeys in northern Wisconsin were more likely to occupy landscapes with less snow cover and a higher percentage of row crops planted in corn. However, we were unable to find supporting evidence in either study area that abandonment of turkeys from survey routes was associated with snow depth or with the percentage of agricultural cover. Spatially, model-predicted estimates of patch-specific occupancy indicated turkey distribution was nonuniform across northern and southeast Wisconsin. Our findings demonstrate that the environmental constraints of turkey occupancy varied across the latitudinal gradient of the state with open cover, snow, and row crops being influential in the north, and agricultural areas and hardwood forest cover important in the southeast. These forces contribute to non-stationarity in wild turkey-environmental relationships. Key habitat-occupancy associations identified in our results can be used to prioritize and strategically target management efforts and resources in areas that are more likely to harbor sustainable turkey populations.</p>
Incidence and influencing factors of occupational pneumoconiosis: A systematic review and meta-analysis
<p><span><strong>Objectives</strong>: </span><span>To determine the incidence of pneumoconiosis worldwide and its influencing factors. </span></p> <p><span><strong>Design</strong>:</span><span> Systematic review and meta-analysis. </span></p> <p><span><strong>Setting</strong>:</span><span> Cohort studies on occupational pneumoconiosis.</span></p> <p><span><strong>Participants</strong>:</span><span> PubMed, Embase, the Cochrane Library, and Web of Science were searched until November 2021. Studies were selected for meta-analysis if they involved at least one variable investigated as an influencing factor for the incidence of pneumoconiosis and reported either the parameters and 95% confidence intervals (CIs) of the risk fit to the data, or sufficient information to allow for the calculation of those values. </span></p> <p><span><strong>Primary outcome measures</strong>:</span><span> The </span><span>pooled incidence of pneumoconiosis and risk ratio (RR) and 95% CIs of influencing factors. </span></p> <p><span><strong>Results</strong>:</span><span> Our meta-analysis included 19 studies with a total of 335,424 participants, of whom 29,972 developed pneumoconiosis. The pooled incidence of pneumoconiosis was 0.093 (95% CI: 0.085~0.135). We identified the following influencing factors: (1) male (RR=3.74; 95%CI 1.31–10.64; P=0.01), (2) smoking (RR=1.80; 95%CI 1.34</span><span>–</span><span>2.43; P=0.0001), (3) tunneling category (RR=4.75; 95%CI 1.96</span><span>–</span><span>11.53; P<0.0001), (4) helping category (RR=0.07; 95%CI 0.13</span><span>–</span><span>0.16; P<0.0001), </span><span>(5) age (the highest incidence occurs between the ages of 50 and 60), </span><span>(6) duration of dust exposure ((RR=4.59, 95% CI 2.41</span><span>–</span><span>8.74, P<0.01), (7) cumulative total dust exposure (CTD) (RR=34.14, 95% CI 17.50</span><span>–</span><span>66.63, P<0.01). A dose-response analysis revealed a significant positive linear dose-response association between the risk of pneumoconiosis and duration of exposure and CTD (P-nonlinearity=0.10, P-nonlinearity=0.16; respectively). The Pearson correlation analysis revealed that silicosis incidence was highly correlated with CSE (r=0.794, P<0.001).</span></p> <p><span><strong>Conclusion</strong>:</span><span> The incidence of pneumoconiosis in occupational workers was 0.093 and seven factors were found to be associated with the incidence, providing some insight into the prevention of pneumoconiosis.</span></p> <p><span>PROSPERO registration number: CRD42022323233.</span></p> <p><span>Abbreviations:</span><span> NOS: Newcastle Ottawa Scale, CWP: Coal Worker's Pneumoconiosis, CTD: cumulative total dust exposure, CSE: cumulative silica exposure.</span></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.