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1,630 results for “Occupancy”
Pika habitat occupancy survey data for Niwot Ridge and Green Lakes Valley, 2016 - ongoing
Long-term monitoring of habitat occupancy can reveal patterns of habitat use, population dynamics, and factors controlling species distribution. The American pika (Ochotona princeps), a small mammal found in rocky habitats throughout western North America, has been targeted for occupancy studies due to its relatively conspicuous behavior and its unusual adaptations for surviving long, cold winters without hibernation. These adaptations include an unusually high resting metabolic rate and maintenance of body temperatures near the lethal maximum for this species, which would appear to compromise the pika's ability to survive warmer summers. Recent monitoring as well as projections based on future climate scenarios have suggested this species is experiencing a period of range retraction due to warming summers and/or loss of insulating winter snow cover. Niwot Ridge is situated ideally to test competing hypotheses about the trajectory and drivers of pika range shift. The pika is still common throughout the Colorado Rockies, but published models differ markedly regarding projections of the pika’s future distribution in this region. Niwot Ridge has experienced warmer summers as well as shorter periods of insulating snow cover in recent years, and there is evidence that pikas are now less common than they once were in at least one area on the ridge. This study is designed to provide robust data on pika population trends through long-term monitoring of occupancy in a spatially balanced random sample of pika habitat patches centered on Niwot Ridge. Survey plots (n = 72) were selected according to a Generalized Random-Tessellation Stratified (GRTS) algorithm, stratified dichotomously by elevation, average annual snow accumulation (SWE), and probabilities of pika occurrence based on previous data. Each plot extends 12 m in radius from a GRTS point. To ensure that each plot contains at least 10% cover of talus, plot coordinates were adjusted (usually less than 50 m) or replaced
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
Content analysis of occupational definitions
<p>The methodological process followed for the competence assignment has considered two main phases. In a first phase, a taxonomic analysis has been carried out, identifying and systematizing the tasks included in around the 1.300 definitions of the two main occupational classification systems SOC-2018 and ISCO-2008. Once systematized, it has been assigned to key competences from the European Reference Framework on Key Competences for Lifelong Learning established by the European Commission (European Council, 2018). In a second phase, and on the basis of the results obtained, an analysis of competences has been developed. To make it possible, it has been established two indexes that measure to what extent a competence is relevant to an occupation, quantifying if such competence is present in all the tasks performed for such occupation or just in some or even if it is not (extension). The second index, specialization, measures to what extent a given competence is the most important of the tasks developed by an occupation, or if on the contrary, it is only one of the required competences.</p>
Named Entity Corpus for Occupational Substance Exposure Assessment
<p>This is a corpus consisting of selected sections (i.e., <em>Abstract, Methods</em> and <em>Results</em>) of scientific research articles concerning occupational exposures to two different types of substance, i.e., diesel exhaust (51 articles) and respirable crystalline silica (RCS) (50 articles). The article sections have been annotated by experts in the field with 6 categories of named entities (NEs) relevant to the assessment of occupational substance exposures, particularly in the context of Job Exposure Matrices (JEMs).</p> <p>The corpus is available in two different formats, <a href="https://brat.nlplab.org/standoff.html">brat standoff format</a> and JSON. </p> <p>The corpus and associated NER models are described in more detail in the following atricle, which should be cited if you use the corpus: </p> <p>Thompson, P., Ananiadou, S., Basinas I., Brinchmann, B. C., Cramer, C., Galea, K. S., Ge, C., Georgiadis, P., Kirkeleit, J., Kuijpers, E., Nguyen, N., Nuñez, R., Schlünssen, V., Stokholm, Z. A., Taher, E. A., Tinnerberg, H., Van Tongeren, M. and Xie, Q. (2024).<a href="https://doi.org/10.1371/journal.pone.0307844"> </a><a href="https://doi.org/10.1371/journal.pone.0307844">Supporting the working life exposome: annotating occupational exposure for enhanced literature search</a>. PLoS ONE 19(8): e0307844</p>
Energy and Emissions Impacts of Atlanta's Reversible Express Toll Lanes and High-Occupancy Toll Lanes
<p>This dataset is the MOVES-Matrix emission rates for the NCST project of Energy and Emissions Impacts of Atlanta’s Reversible Express Toll Lanes and High-Occupancy Toll Lanes, developed by our research team at Georgia Institute of Technology.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p>This report summarizes the impact on corridor-level energy use and emissions associated with the 2018 opening of the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA. The research team tracked changes in vehicle throughput on the managed lane corridors (extracted from GDOT’s Georgia NaviGAtor machine vision system after comprehensive QA/QC) and performed a difference-in-difference analysis to exclude regional changes, pairing test sites vs. control sites not influenced by the openings. The results show a large increase in overall peak-period vehicle throughput on the NWC, especially on I-575, due to the congestion decrease (20 mph speed increases at some locations). The increase in corridor-level energy use and emissions was smaller than vehicle throughput, but still significant. Predicted downwind maximum CO concentrations only increased from 1.81 ppm to 1.93 ppm(which remains extremely low). The increase in morning peak activity on the corridor likely resulted from diversion of some traffic into the peak from the shoulder periods, diversion of some traffic from other nearby freeway corridors, and diversion of local road traffic into the corridor. Unfortunately, without overall control volume totals and/or pre-and-post travel behavior surveys for the alternative commute routes, it is not possible to quantify the likely reductions in traffic flow and emissions that occurred along the other corridors that likely resulted from morning commute shifts. Hence, the team cannot draw reliable conclusions related to net regional or sub-regional impacts associated with the new managed lane corridors. The impact observed on the I-85 corridor was much smaller than on the NWC, especially at Indian Trail/Lilburn Road (far from the Express Lane Extension). After the Express Lanes opened, energy use and emission rates at Old Peachtree Road increased slightly (as uncongested vehicle speeds increased), but this increase may be short-lived as traffic on the corridor changes over time.</p> <p> </p>
Monthly maps of Warm Core Ring Occupancy and occurrences of Salinity Maximum Intrusions in the Slope Sea (1990-2019)
<p>This dataset presents two important variables across the shelfbreak in the Northwest Atlantic: (i) Warm Core Ring Occupancy in the Slope sea proximate to the shelfbreak; and (ii) locations of Salinity maximum intrusions in the shelf. Monthly fields of both of these fields together are presented for the period 1990-2019 with file name format <em>smax_ring_mm_yyyy.jpg. </em>The gray and red dots on the shelf represent locations of profiles taken from the Ecosystem Monitoring Program’s (EcoMon) hydrographic data (available from the National Centers for Environmental Information World Ocean Database accessible at <a href="http://www.ncei.noaa.gov/products/world-ocean-database">www.ncei.noaa.gov/products/world-ocean-database</a>). Red dots show locations of profiles which contained mid-depth salinity maximum intrusions, gray dots are profiles without any mid-depth salinity maximum intrusion. Profiles with intrusions were identified using the methodology of Gawarkiewicz et al., 2022. The ring occupancy was calculated from a Warm Core Ring Tracking dataset with ring tracks from 2000-2010 and 2011- 2020 available from Zenodo (<a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>, <a href="https://doi.org/10.5281/zenodo.7406675">https://doi.org/10.5281/zenodo.7406675</a>) and ring trajectories from 1978 through 1999 available from the Bedford Institute of Oceanography, Canada. To calculate the ring occupancy the region was sub-divided into 0.1 by 0.1 degree bins. Ring trajectories and approximate geographical range (calculated from the ring area, assuming the ring is a perfect circle) were overlain on this region and the days rings are present in each bin are counted in units of ring days. A ring day is the presence of one single ring in a bin during a given day. These ring day counts were converted to percentages, dividing by days in the given month and multiplying by 100. For more details see Silver et al., 2022 and Salois et al., 2023. From these figures one can see the spatial relationship between Warm Core Rings and Salinity Maximum Intrusions, with clusters of intrusions occurring in areas adjacent to high ring occupancy. </p> <p>An animation of two particular years is also presented in <em>movie_smax_ring_1993_2012.gif</em> to highlight this relationship: Low ring year (1993) leading to fewer Smax intrusion and high ring year (2012) leading to more intrusions.</p> <p> </p> <p>Gawarkiewicz, G., Fratantoni, P., Bahr, F., & Ellertson, A. (2022). Increasing Frequency of Mid‐Depth Salinity Maximum Intrusions in the Middle Atlantic Bight. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(7), e2021JC018233. <a href="https://doi.org/10.1029/2021JC018233">https://doi.org/10.1029/2021JC018233</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Salois, S. L., Hyde, K. J., Silver, A., Lowman, B. A., Gangopadhyay, A., Gawarkiewicz, G., ... & Lapp, M. (2023). Shelf break exchange processes influence the availability of the northern shortfin squid, Illex illecebrosus, in the Northwest Atlantic. <em>Fisheries Oceanography</em>. <a href="https://doi.org/10.1111/fog.12640">https://doi.org/10.1111/fog.12640</a> </p>
Common Raven (Corvus corax) Occupancy Survey and Habitat Selection Data in Cliff Habitat of the Central Appalachian Region, USA, 2009-2010
We identified 24 cliff sites across four states of the Central Appalachian Region of the eastern USA (Kentucky, North Carolina, Virginia, and West Virginia) with known raven occupancy at which to perform occupancy surveys for estimating detection probability and the effects of covariates. We surveyed each cliff site 2-4 times in either 2009 or 2010 and recorded time-to-first detection and time to confirmed cliff occupancy during a two-hour survey. Daily surveys were completed between 06:00 and local solar noon. During each survey, we recorded covariates, including air temperature at survey start time, cloud cover, wind speed, and day of year. We also calculated the distance of the observation point from the cliff being surveyed and the forest cover around the cliff. We also collected data thought to be pertinent for habitat selection by ravens on 26 cliffs occupied by ravens and 26 cliffs deemed unoccupied by ravens in 2010. For each cliff, we measured cliff physiographic characteristics, such as cliff length, cliff height, and occlusion by vegetation, and landscape characteristics, including percent forest and urban cover around the cliff and distances from the cliff to the nearest road and human habitation.
Site occupancy matrices, The River Ouse Project
<p>The <a href="http://www.sussex.ac.uk/riverouse/">River Ouse Project</a> was started by Dr Margaret Pilkington and colleagues in the Centre for Continuing Education, University of Sussex. Margaret is now retired with emeritus status and continues to run the project with a team of volunteers, in association with the University of Sussex. The team does botanical surveys of streamside grassland and steep wooded valleys (gills) in the upper reaches of the Sussex Ouse, a short flashy river arising on the southern slopes of the High Weald AONB (Area of Outstanding Natural Beauty). Survey sites are chosen on the basis of species richness, potential for restoration and contribution to flood control, and surveyed using the sampling methods outlined in Rodwell, J S (1992. British Plant Communities, Volume 3, Grasslands and Montane Communities). Survey data are transferred from the paper record taken in the field to Excel spreadsheets, and from there after validation and cleaning into two MySQL (MariaDB) databases, meadows and gills.</p> <p>The file is an extract from the meadows database. It contains binary data of the site occupancy for most of the plants encountered in meadow sites (stands, assemblies) sampled using five 2m x 2m quadrats. Details of the database are available here: <a href="https://zygodon.github.io/River-Ouse-Project-databases/">River Ouse Project databases</a>. </p> <p>For further details and access to the full database contact the author.</p>
Data for "How do ecologists estimate occupancy in practice?" by Goldstein et al.
<p> Data for review of occupancy estimtaion methods by Goldstein et al.</p> <p> </p> <p>Please see the accompanying manuscript for full methodology. We will link to it when the manuscript is published.</p> <p> </p> <p>This upload contains three datasets: "binary_scores_phase1.csv", "binary_scores_phase2.csv" and "modsel_results.csv". Each is a .csv file containing data from a survey of occupancy estimation practices. Each row represents one peer-reviewed paper, while each column represents a characteristic of the paper. Most columns are TRUE/FALSE, indicating whether or not the paper satisfied the relevant criterion.</p> <p> </p> <p>One additional raw resource is provided. The .zip file "all_papers_2022-04-07.zip" contains 6 .xls files giving the full set of papers returned by the original Web of Science search. These are unmodified from the initial search.</p> <p> </p> <p>All datasets contain the column:</p> <p>ID - A unique ID for each paper; it most cases, a DOI. When Web of Science returned an invalid DOI, the ID is set to the paper title instead.</p> <p> </p> <p>Note that all papers in Phase 2 are also in Phase 1, and all model selection papers are in both Phase 1 and Phase 2. The ID column can be used to join datasets.</p> <p> </p> <p>Across both datasets, if all options in a category are FALSE or if a field is NA, that may mean that the review team was unable to determine what choices the authors made.</p> <p> </p> <p>binary_scores_phase1.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>coll_newdata - Did the authors analyze newly collected data?</p> <p>coll_existing - Did the authors analyze existing, previously published data?</p> <p>coll_longterm - Did the authors analyze data produced by a long-term monitoring program?</p> <p>coll_particip - Did the authors analyze participatory science data?</p> <p>eco_frshwtr - Was the study system a freshwater ecosystem?</p> <p>eco_marine - Was the study system a marine ecosystem?</p> <p>eco_terra - Was the study system a terrestrial ecosystem?</p> <p>region_USA - Were the data collected in the USA?</p> <p>region_NoAm - Were the data collected in North America?</p> <p>region_CenAm - Were the data collected in Central America?</p> <p>region_SoAm - Were the data collected in South America?</p> <p>region_Africa - Were the data collected in Africa?</p> <p>region_Eur - Were the data collected in Europe?</p> <p>region_Asia - Were the data collected in Asia?</p> <p>region_Oceania - Were the data collected in Oceania?</p> <p>framework_MLE - Did the authors estimate models in a maximum likelihood framework?</p> <p>framework_ML - Did the authors estimate models in a machine learning framework?</p> <p>framework_Bayes - Did the authors estimate models in a Bayesian framework?</p> <p>gof_AUC - Did the authors use area-under-the-curve to evaluate their models?</p> <p>gof_CV - Did the authors use cross validation to evaluate their models?</p> <p>gof_PPC - Did the authors use poserior predictive checks to evaluate their models?</p> <p>gof_parboot - Did the authors use parametric bootstrapping to evaluate their models?</p> <p>gof_bayespv - Did the authors use Bayesian p-values to evaluate their models?</p> <p>gof_any - Did the authors conduct any model checking?</p> <p>taxon_mammal - Were some or all of the study species mammals?</p> <p>taxon_bird - Were some or all of the study species birds?</p> <p>taxon_herp - Were some or all of the study species herptiles (reptiles and amphibians)?</p> <p>taxon_fish - Were some or all of the study species fish?</p> <p>taxon_arthro - Were some or all of the study species arthropods?</p> <p>taxon_othinv - Were some or all of the study species non-arthropod invertebrates?</p> <p>soft_unmarked - Did the authors estimate models using the software unmarked?</p> <p>soft_PRESENCE - Did the authors estimate models using PRESENCE-family software?</p> <p>soft_MARK - Did the authors estimate models using MARK-family software?</p> <p>soft_JAGS - Did the authors estimate models using JAGS-family software?</p> <p>soft_lme4 - Did the authors estimate models using the software lme4?</p> <p>soft_MaxEnt - Did the authors estimate models using the software MaxEnt?</p> <p>soft_baseR - Did the authors estimate models using custom models written in base-R?</p> <p>soft_NR - Did the authors fail to clearly report what software they used to estimate models?</p> <p>soft_other - Did the authors estimate models using some other software?</p> <p>nspecies - How many species did the authors study?</p> <p>nspec_1 - Did the authors analyze data on exactly 1 species?</p> <p>nspec_2 - Did the authors analyze data on exactly 2 species?</p> <p>nspec_3_5 - Did the authors analyze data on 3-5 species?</p> <p>nspec_6_10 - Did the authors analyze data on 6-10 species?</p> <p>nspec_11_20 - Did the authors analyze data on 11-20 species?</p> <p>nspec_20plus - Did the authors analyze data on more than 20 species?</p> <p>compare_avg - Did the authors conduct model averaging?</p> <p>compare_sel - Did the authors conduct model selection?</p> <p>compare_other - Did the authors compare multiple models without averaging or selecting between them?</p> <p>modsel_AICc - Did the authors use AICc in model selection or averaging?</p> <p>modsel_AIC - Did the authors use AIC in model selection or averaging?</p> <p>modsel_other - Did the authors use another information criterion in model selection or averaging?</p> <p>dattype_DND - Were some or all of the data collected as detection-nondetection data?</p> <p>dattype_count - Were some or all of the data collected as count data?</p> <p>dattype_PO - Were some or all of the data collected as presence-only data?</p> <p>dattype_other - Were some or all of the data collected in some other form?</p> <p>survey_visual - Were some or all of the data collected using in-person visual surveys?</p> <p>survey_audio - Were some or all of the data collected using in-person audio surveys?</p> <p>survey_camtrap - Were some or all of the data collected using camera trap surveys?</p> <p>survey_capture - Were some or all of the data collected using animal capture surveys?</p> <p>survey_sign - Were some or all of the data collected using sign surveys?</p> <p>survey_passaudio - Were some or all of the data collected using passive acoustic surveys?</p> <p>survey_DNA - Were some or all of the data collected using eDNA surveys?</p> <p>survey_other - Were some or all of the data collected using some other survey protocol?</p> <p>modtype_SSOM - Did the authors analyze data with an SSOM?</p> <p>modtype_DynOcc - Did the authors analyze data with a dynamic occupancy model?</p> <p>modtype_GLM - Did the authors analyze data with a GLM?</p> <p>modtype_cooccur - Did the authors analyze data with a multispecies co-occurrence model?</p> <p>modtype_community - Did the authors analyze data with a multispecies community model?</p> <p>modtype_MaxEnt - Did the authors analyze data with a MaxEnt model?</p> <p>modtype_other - Did the authors analyze data with another model?</p> <p>affil_acad - Did any of the authors have an academic affiliation?</p> <p>affil_govt - Did any of the authors have a government affiliation?</p> <p>affil_other - Did any of the authors have a private or NGO affiliation?</p> <p> </p> <p> </p> <p>binary_scores_phase2.csv gives the results of Phase 2 of the review. It contains the following columns:</p> <p> </p> <p>code_avail - Did we determine that the authors published their model fitting code?</p> <p>data_avail - Did we determine that the authors published their data?</p> <p>nsite - At how many sites were data collected?</p> <p>nsite_lt20 - Were data collected at 20 or fewer sites?</p> <p>nsite_21_50 - Were data collected at 20-50 sites?</p> <p>nsite_51_100 - Were data collected at 51-100 sites?</p> <p>nsite_101p - Were data collected at more than 100 sites?</p> <p>time_pds - Over how many primary time periods (e.g. sampling seasons) were data collected?</p> <p>time_pds_1 - Were data collected during a single time period?</p> <p>time_pds_2_3 - Were data collected during 2-3 time periods?</p> <p>time_pds_4p - Were data collected during 4 or more time periods?</p> <p>nrepl - Roughly how many replicate surveys were collected per site?</p> <p>nrepl_1 - Was only one replicate survey conducted per site?</p> <p>nrepl_2_3 - Were 2-3 replicate surveys conducted per site?</p> <p>nrepl_4_5 - Were 4-5 replicate surveys conducted per site?</p> <p>nrepl_6p - Were 6 or more replicate surveys conducted per site?</p> <p>window - What sampling window was used to discretize continuous-time sampling? (continuous-time studies only; otherwise NA)</p> <p>det_window_lt_day - Was a sampling unit of less than one day used to discretize sampling?</p> <p>det_window_1day - Was a sampling unit of one day used to discretize sampling?</p> <p>det_window_2_6day - Was a sampling unit of 2-6 days used to discretize sampling?</p> <p>det_window_7_14day - Was a sampling unit of l7-14 days used to discretize sampling?</p> <p>det_window_15p_day - Was a sampling unit of 15 days or more used to discretize sampling?</p> <p>homerange_is_bigger - Did the authors describe their survey area per site as bigger than the target species' home range?</p> <p>homerange_is_smaller -Did the authors describe their survey area per site as smaller than the target species' home range?</p> <p>informative_priors - Did the authors use informative priors in a Bayesian analysis?</p> <p>time_in_mod_as_covar - Did the authors include primary time periods in the model as a covariate?</p> <p>time_in_mod_separate_model - Did the authors use separate models to analyze data collected during different primary time periods?</p> <p>time_in_mod_other - Did the authors include primary time periods in the model in some other way?</p> <p>ncovar_det - How many covariates were included in the best model's detection submodel?</p> <p>ncovar_det_zero - Did the detection submodel include 0 covariates?</p> <p>ncovar_det_1_3 - Did the detection submodel include 1-3 covariates?</p> <p>ncovar_det_4p - Did the detection submodel include 4 or more covariates?</p> <p>ncovar_occ - How many covariates were included in the best model's occupancy submodel?</p> <p>ncovar_occ_zero - Did the occupancy submodel include 0 covariates?</p> <p>ncovar_occ_1_3 - Did the occupancy submodel include 1-3 covariates?</p> <p>ncovar_occ_4p - Did the occupancy submodel include 4 or more covariates?</p> <p>covars_both_mods - Were any variables considered in both submodels?</p> <p>model_ranefs - Did the authors include any random effects?</p> <p>model_expl_spatial - Did the model have an explicit spatial component?</p> <p>motivating_q_range - Was range estimation a main goal motivating the study?</p> <p>motivating_q_drivers - Was identifying drivers of occupancy a main goal motivating the study?</p> <p>motivating_q_trends - Was identifying trends in occupancy a main goal motivating the study?</p> <p>motivating_q_predict - Was predicting occupancy under new conditions a main goal motivating the study?</p> <p>motivating_q_theoretical - Was advancing ecological theory a main goal motivating the study?</p> <p>motivating_q_field_method - Was evaluation of a field method a main goal motivating the study?</p> <p>motivating_q_model_method - Was evaluation of a modeling method a main goal motivating the study?</p> <p>context_conservation - Did the authors contextualize their study as relevant to conservation?</p> <p>context_management - Did the authors contextualize their study as relevant to wildlife management?</p> <p>context_natural_hist - Did the authors contextualize their study as relevant to studying natural history of target species?</p> <p>context_methodology - Did the authors contextualize their study as advancing methodology?</p> <p>detdensity - Did the authors mention the possibility that detection and animal density were confounded?</p> <p>interp_top_mod_as_biol - Did the authors interpret model selection results as evidence for a biological process?</p> <p>nmod_reported_best - Did the authors report only the best model from a model selection workflow?</p> <p>nmod_reported_some - Did the authors report multiple model results from a model selection workflow?</p> <p>nmod_reported_all - Did the authors report all model results from a model selection workflow?</p> <p>priors_reported - Did the authors report their priors in a Bayesian workflow?</p> <p>violation_nonindependence - Do the authors acknowledge violating the assumption of independent data?</p> <p>violation_movement - Do the authors acknowledge violating the assumption of no animal movement?</p> <p>violation_demography - Do the authors acknowledge violating the assumption of no demographic change?</p> <p>violation_det_heterogeneity - Do the authors acknowledge violating the assumption of no unmodeled heterogeneity in detection?</p> <p>violation_yes_other - Do the authors acknowledge violating another assumption?</p> <p>violation_explicit_no - Do the authors state that all assumptions were met?</p> <p>hypotheses_all - Do the authors provide hypotheses for the effect of all covariates?</p> <p>hypotheses_some - Do the authors provide hypotheses for the effect of some covariates?</p> <p>interpret_det_literal - Do the authors interpret detection literally?</p> <p>interpret_det_biol - Do the authors interpret detection as confounded with biology?</p> <p>interpret_vars_significant - Do the authors interpret some variables as significant based on p-values?</p> <p>interpret_vars_credible - Do the authors interpret some variables as meaningful based on Bayesian credible intervals?</p> <p> </p> <p> </p> <p>modsel_results.csv describes the model selection choices made by 64 Phase 2 papers that conducted model selection. In addition to ID, it contains two columns:</p> <p> </p> <p>Submodel approach - Did the authors use a separate-by-submodel approach to model selection, did they only conduct model selection on one submodel, or did they perform variable selection on both submodels simultaneously?</p> <p>Candidate set approach - Did the authors conduct model selection among a set of a priori candidate models, or did they select between arbitrary models based on combinations of all covariates?</p> <p> </p> <p> </p> <p> </p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
Linguistic-symbolic classification of occupations
<p>As a result, it has been considered the following occupational categories, based on the degree of symbolic analysis and language intensity: (A) high symbolic analysts, (B) low symbolic analysts, (C) high intensity oral interaction with public (high public service), (D) low intensity oral interaction with public (low public service), and (E) manual labor occupations with limited symbolic and oral demands. Further, and within (B) category -low symbolic analysts-, it is distinguished between (B1) those whose work is generally inside the organization (such as file clerks) and (B2) those whose work includes interacting with the public (such as receptionists). In a similar way, (C) category is also divided into (C1) category of nurses and (C2-C5) which group the remainder of the high public service occupations.</p> <p>The result of the categorization of occupations by language use is summarized in the table below:</p> <table> <thead> <tr> <th>Major occupational classification</th> <th>Linguistic characteristics of occupation</th> <th>Sub classification</th> <th>Example of occupation</th> </tr> </thead> <tbody> <tr> <td>A: High symbolic analysts</td> <td>Produce/consume long or complex written communications, with variable but often important oral communication</td> <td>A1. Upper management</td> <td>Chief executive, human resource executive</td> </tr> <tr> <td> </td> <td> </td> <td>A2. Professionals</td> <td>Lawyer, doctor</td> </tr> <tr> <td> </td> <td> </td> <td>A3. Lower management</td> <td>First line manager/supervisor</td> </tr> <tr> <td> </td> <td> </td> <td>A4. High symbolic analysts, not managers</td> <td>Public relations specialists, computer systems specialists</td> </tr> <tr> <td>B: Low symbolic analysts</td> <td>Produce/consume short or simple written communications, with variable but often important oral communication</td> <td>B1. Low symbolic analysts with low likelihood of public interaction</td> <td>File clerks</td> </tr> <tr> <td> </td> <td> </td> <td>B2. Low symbolic analysts with high likelihood of public interaction</td> <td>Receptionists, billing/appointment clerks</td> </tr> <tr> <td>C: In-person service workers with high communicative demands</td> <td>Important oral communication, limited but present written skills, and high public interaction</td> <td>C1. Nurses</td> <td>Nurses</td> </tr> <tr> <td> </td> <td> </td> <td>C2. Assistants and technicians in public service settings</td> <td>Medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C3. Police, etc.</td> <td>Police, detectives, investigators</td> </tr> <tr> <td> </td> <td> </td> <td>C4. Firefighters, emergency medical technicians</td> <td>Firefighters, emergency medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C5. Miscellaneous</td> <td>Counselors, dispatchers</td> </tr> <tr> <td>D. In-person service workers with low communicative demands</td> <td>Simple oral communication and public interaction, very limited or no writing</td> <td>(no subcategories in our study)</td> <td>home health care aides, security guards</td> </tr> <tr> <td>E. Manual work</td> <td>Limited oral and written consumption and production</td> <td>E1. Skilled manual work</td> <td>Plumber</td> </tr> <tr> <td> </td> <td> </td> <td>E2. Unskilled manual work</td> <td>Janitor</td> </tr> </tbody> </table> <p> </p>
PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"
<p>This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper:</p> <p>Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv.</p> <p>Contents:</p> <p>AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis.</p> <p>AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function.</p> <p>AV_2clusters.rds: dataset for the calculation of the artifact metrics.</p> <p>AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis.</p> <p>AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm.</p> <p>AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis.</p> <p>AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.</p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Occupations on the map: Using a super learner algorithm to downscale labor statistics, data
<p>This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm.</p> <p>In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.</p>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</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.