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Comparing mail-in self-collected specimens sent via United States Postal Service versus clinic-collected specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae in extra-genital sites data set
<p>This data set was used to evaluate the concordance between clinic-collected extra-genital specimens and self-collected mailed-in extra-genital specimens among participants seeking sexually transmitted infection testing at a free clinic in Hollywood, CA. The newest version of the file reflects sample adequacy control (SAC) cycle threshold values for each participant.</p>
Dataset for "Disproportionate Clean Air Act violations occur in communities of color throughout the United States"
<div> <p>This is the dataset (ver 2024.04.25) supporting the manuscript "Disproportionate Clean Air Act violations occur in communities of color throughout the United States" in the journal <em>Environmental Research Letters </em>(DOI: 10.1088/1748-9326/ad416d). A description of the data files and scripts can be found in the README. </p> </div>
FIGURE 7 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 7. Dorsal views of heads. (A) Medusapyga alsea. (B) M. chehalis. Scale bars 100 µm.
FIGURE 8 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 8. Dorsal views of pronota. (A) Medusapyga alsea. (B) M. chehalis. Scale bars 100 µm.
FIGURE 2 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 2. Dorsal habitus. (A) Medusapyga alsea, female. (B) M. chehalis, female. Scale bar 1 mm.
Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor
<p>Dataset and code to replicate the paper:</p> <p>Orlandi et al., "Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor"</p>
Multibay unit-load pre-marshalling problem instances
<p>This dataset provides instances for experiments of the multibay unit-load pre-marshalling problem. For each experiment, we provide the instances as JSON-files.</p> <p> </p> <p>The folder exp_instances_tier_1 includes 27720 instances with one tier.</p> <p>The folder exp_instances_tier_2 includes 27720 instances with two tiers.</p> <p> </p> <p>The JSON-files contain the following information:</p> <ul> <li>warehouse_layout: Number of bays arranged in a square.</li> <li>bay_layout: Size of each bay. </li> <li>fill_level: Share of occupied slots in the warehouse.</li> <li>max_priority: Number of priority classes.</li> <li>seed: Seed used to generate the instance.</li> <li>access_directions: Allowed access directions.</li> <li>warehouse_state: Priority class for each slot defined by x, y, and z coordinates. Priority class zero corresponds to an empty slot.</li> <li>access_points: Access points in the warehouse in front of each bay. Each access point is located by x and y coordinates.</li> <li>robot_positions: Randomly generated initial positions for robots defined by x and y coordinates.</li> </ul>
Data for climate-resilient snowpack estimation in the Western United States
<p>Generated and preprocessed files for the resilient snowpack estimation project. All preprocessed data were originally produced by the WUS-D3 project (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6) or PRISM (https://www.prism.oregonstate.edu/).</p>
GHCN-daily: Western United States precipitation dataset
<p>This is a dataset of daily precipitation totals in the western United States (US) from January 1 1950 – February 29 2024. The dataset is based on daily precipitation measurements from 642 gauges across the western US, with periodic data gap-filling perfomed using estimates based on nearby gauges. From the daily records at the 642 gauge sites we produced a gridded record of daily precipitation totals at 0.25-degree resolution across the western US. The 642 gauges used as the basis for this dataset were selected for their uniquely long records and continuous coverage from the early 1950s through 2023. The purpose of producing this dataset was for evaluation of trends in sub-monthly cool-season (November–March) precipitation characteristics.</p>
Data from: Scaling of leaf area with biomass in trees reconsidered: Constant metabolically active sapwood volume per unit leaf area with height growth
<p>Hypoallometric (slope<1) scaling between metabolic rate and body mass is often regarded as near-universal across organisms. However, there are compelling reasons to question hypoallometric scaling in woody plants, where metabolic rate=leaf area. This leaf area must provide carbon to the metabolically active sapwood volume (VMASW). Within populations of a species, variants in which VMASW increases per unit leaf area with height growth (e.g. ⅔ or ¾ scaling) would have proportionally less carbon for growth and reproduction as they grow taller. Therefore, selection should favor individuals in which, as they grow taller, leaf area scales isometrically with shoot VMASW (slope=1). Using tetrazolium staining, we measured total VMASW and total leaf area (LAtot) across 22 individuals of Ricinus communis and confirmed that leaf area scales isometrically with VMASW, and that VMASW is much smaller than total sapwood volume. With the potential of the LAtot-VMASW relationship to shape factors as diverse as the crown area-stem diameter relationship, conduit diameter scaling, reproductive output, and drought-induced mortality, our work suggests that the notion that sapwood increases per unit leaf area with height growth requires revision.</p>
DayCent-UV grassland productivity across the midwestern and western conterminous United States.
<p>This dataset provides information about daily and annual simulated aboveground plant productivity (ANPP) in grasslands grid cells across the midwestern and western conterminous United States from 1984 to 2015. Annual ANPP estimates from Rangeland Analysis Platform (RAP) are included for comparison.</p> <p><strong>Files Description:</strong></p> <ul> <li><strong>Grid_Cells_Shapefile.zip</strong></li> </ul> <p>Contains<strong> </strong>the spatial distribution for each simulated grassland grid cell. Each grid cell is identified by a <code>grid</code> number ranging from 0 to 2712, a <code>col</code> number increasing from west to east, and a <code>row</code> number increasing from south to north. </p> <ul> <li><strong>Daily_ANPP_DayCent-UV.zip:</strong></li> </ul> <p>Each NetCDF file contains the daily ANPP information for each grid cell during a specifc year. Daily ANPP for a grid cell can be acessed in the <code>cropANPP</code> variable, which depends on the grid cell identifier <code>row</code> and <code>col</code>, and <code>time</code> for the day of the year.</p> <p>For example, using the library <em>xarray</em> from Python, the daily ANPP value for a California grassland grid cell (grid : 1207, row: 78, col: 24) in day 150 in year 2000 can be obtained by using the following command lines:</p> <p>import xarray as xr</p> <p>dataset = xr.open_dataset( folder_directory + '/Daily_ANPP_DayCent-UV/daycent_cwrf_2000.nc')</p> <p>time = 150</p> <p>lon_index = 24</p> <p>lat_index = 78 </p> <p>value = dataset.cropANPP[time, lat_index, lon_index].values</p> <p>To calculate annual ANPP for each grid cell, sum the <code>cropANPP</code> values across all days within a file. </p> <ul> <li><strong>Annual_ANPP_DAYCENT-UV_1986-2015.csv</strong></li> </ul> <p>Contains the simulated annual ANPP in the <code>cum_ANPP</code> column (gC/m²), for each <code>year</code> and each grid cell identified by <code>grid</code>, <code>lon</code>, <code>lat</code> ,<code>row</code>, <code>col</code> (see shapefile description)</p> <ul> <li><strong>Annual_ANPP_RAP_1986-2015.csv</strong></li> </ul> <p>Provides RAP's annual ANPP estimates for each grid cell, identified by its <code>grid</code> number, by using two different equations: </p> <ul> <li> <ul> <li>Gerardhi & Sala equation: RAP-ANPP estimates are located in column <code>RAP_Sala_biomass</code> (g Biomass/m²), together with the annual fraction of carbon allocated to shoots in <code>fANPP_Sala</code></li> <li>Hiu equation: RAP-ANPP estimates are located in column <code>RAP_Hiu_biomass</code>, together with the annual fraction of carbon allocated to shoots in <code>fANPP_Hiu</code>.</li> </ul> </li> </ul> <p>We assumed a factor of 2.4 (gBiomass/m²)/(gC) to convert gC/m² to gBiomass/m².</p>
Geocoded variant of United Nations Code for Trade and Transport Locations
<div></div> <div> <div> <div> <div>The <a href="https://unece.org/trade/uncefact/unlocode">United Nations Code for Trade and Transport Locations</a> dataset provides information on the codes known as "UN/LOCODE". </div> <div>This version of the dataset, based on the csv files of version 232, has extra information based on pycountry (names of administrative regions, country names, flags) and on most important on location.</div> <div> </div> <div> </div> <div>The dataset has the following extra fields:<br> <table> <tbody> <tr> <td>Column name</td> <td>Example</td> <td>Description</td> <td>Source</td> </tr> <tr> <td> <pre>alpha_2_country</pre> </td> <td> <pre>AD</pre> </td> <td>2 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>alpha_3_country</pre> </td> <td> <pre>AND</pre> </td> <td>3 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>flag_country</pre> </td> <td> <pre>🇦🇩</pre> </td> <td>unicode flag</td> <td>pycountry</td> </tr> <tr> <td> <pre>name_country</pre> </td> <td> <pre>Andorra</pre> </td> <td>Country name, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>numeric_country</pre> </td> <td> <pre>020</pre> </td> <td>Country code</td> <td>pycountry</td> </tr> <tr> <td> <pre>official_name_country</pre> </td> <td> <pre>Principality of Andorra</pre> </td> <td>Country official name</td> <td>pycountry</td> </tr> <tr> <td> <pre>score</pre> </td> <td>95</td> <td>Confidence code of geocoding [0-100/nan]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geocoded</pre> </td> <td>True</td> <td>Was the coordinate derived from geocoding [true/false]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geometry</pre> </td> <td> <pre>POINT (1.516666666666667 42.5)</pre> </td> <td>Point coordinate</td> <td>ArcGIS if geocoded else original UNLO </td> </tr> </tbody> </table> <br>The figure below shows the locations (n=23663) that have been geocoded in this dataset in green and the existing locations in pink. </div> <div><br></div> <div> </div> <div>Ownership of the original data recides at UNLO and the participants. </div> </div> </div> </div>
Replication package of "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation"
<p>Replication package for the paper "Higher Fault Detection Through Novel Density Estimators in Unit Test Generation" accepted at the Symposium on Search-based Software Engineering (SSBSE) 2024.</p> <p> </p> <p> </p>
Data from: Beyond the colony-forming-unit: Rapid bacterial evaluation in Osteomyelitis
<p>Examination of bacteria/host cell interactions is important for understanding the aetiology of many infectious diseases. The colony-forming-unit (CFU) has been the standard for quantifying bacterial burden for the past century, however, this suffers from low sensitivity and is dependent on bacterial culturability in vitro. Our data demonstrate the discrepancy between the CFU and bacterial genome copy number in an osteomyelitis-relevant co-culture system and we confirm diagnosis and quantify bacterial load in clinical bone specimens. This study provides insight into improving the quantification of bacterial burden in such cases.</p>
Homogenisation of daily temperature and humidity series in the United Kingdom
<p>Data to accompany the publication "Homogenisation of daily temperature and humidity series in the United Kingdom". Building on previous experience with continental and global data sets, we use a quantile-matching approach to homogenise temperature and humidity series measured by a network of 220 stations in the United Kingdom (UK). The data set spans 160 years at daily resolution, although data coverage varies greatly in time, space, and across variables.</p>
Data from: Analysis of genotyping data reveals the unique genetic diversity represented by the breeds of sheep native to the United Kingdom
<p><strong>Background: </strong>Sheep breeds native to the United Kingdom are noted for high breed variability and exhibit a striking diversity of different traits in phenotypes and genetic diversity. Some of these traits are highly sustainable, such as seasonal wool shedding in the Wiltshire Horn, are likely to become more important as pressures on sheep production increase in coming decades. Despite their clear importance to the future of sheep farming, the genetic diversity of native UK sheep breeds is poorly characterised. This increases the risk of losing the ability to select for breed-specific traits from native breeds that might be important to the UK sheep sector in the future. Here, we use 50K genotyping to perform preliminary analysis of breed relationships and genetic diversity within native UK sheep breeds, as a first step towards a comprehensive characterisation. This study generates novel data for thirteen native UK breeds, including 6 on the UK Breeds at Risk (BAR) list, and utilises existing data from the publicly available Sheep HapMap dataset to investigate population structure, heterozygosity and admixture.</p> <p><strong>Results: </strong>In this study the commercial breeds exhibited high levels of admixture, weaker population structure and had higher heterozygosity compared to the other native breeds, which generally tend to be more distinct, less admixed, and have lower genetic diversity and higher kinship coefficients. Some breeds including the Wiltshire Horn, Lincoln Longwool and Ryeland showed very little admixture at all, indicating a high level of breed integrity but potentially low genetic diversity. Population structure and admixture were strongly influenced by sample size and sample provenance – highlighting the need for equal sample sizes, sufficient numbers of individuals per breed, and sampling across multiple flocks. The genetic profiles both within and between breeds were highly complex for UK sheep, reflecting the complexity in the demographic history of these breeds.</p> <p><strong>Conclusion: </strong>Our results highlight the utility of genotyping data for investigating breed diversity and genetic structure. They also suggest that routine generation of genotyping data would be very useful in informing conservation strategies for rare and declining breeds with small populations sizes. We conclude that generating genetic resources for the sheep breeds that are native to the UK will help preserve the considerable genetic diversity represented by these breeds, and safe guard this diversity as a valuable resource for the UK sheep sector to utilise in the face of future challenges.</p>
Data from: The role of predation, forestry and productivity in moose harvest at different spatial levels of management units
<p>Management of ungulate populations to the desired density and/or demographic composition are challenged by contrasting aims of different stakeholders. For example, hunters may want to maximize hunting opportunities whereas commercial forest owners may want to minimize moose densities to mitigate browsing damage. In addition, the return of large predators such as wolves (<em>Canis lupus</em>) affects the possible harvest yield of ungulates and influences the population composition through their selection of specific age classes. The aim of this study was to gain a better understanding of factors related to the variation in moose (<em>Alces alces</em>) harvest. We used moose harvest statistics from the period 2012-2020, wolf annual monitoring data, annual brown bear (<em>Ursus arctos</em>) density, proportion of young forest per management unit, and proportion of agricultural land per management unit (index for productivity and roe deer (<em>Capreolus capreolus</em>) density) to explain variation in moose harvest across different management units at two spatial levels in two bordering countries, Sweden and Norway. The results showed variable responses in total harvest to changes in wolf territory density both at the regional and local management level. The proportion of young forest was correlated with both increased total harvest and proportion of calves. Increased proportion of agricultural land was linked to both increased total harvest and proportion of calves, likely due to that increased roe deer densities re-directed wolf predation from moose to roe deer, and an inverse relationship with brown bear density. Differences between countries may be due to differences in the management regime of moose, both in an historical and present perspective. Improved monitoring for individual hunting areas over time will be important for both the understanding of how different ungulate populations are affected by various factors and for the desired management of wildlife populations shared across borders.</p>
Figure 1 in A new species of Paranthrene Hübner (Lepidoptera: Sesiidae) from the northern midwest United States
Figure 1. Paranthrene tabaniformis, habitus, dorsal view.
Adaptive Parameter Control for Search-Based Unit Test Generation — Replication Package
<h1>Running the experiments</h1> <p>Prerequisites for running the experiments:</p> <ul> <li><a href="https://www.docker.com/" target="_blank" rel="noopener">Docker</a></li> <li><a href="https://python-poetry.org/" target="_blank" rel="noopener">Poetry</a></li> </ul> <p>Steps to run the experiments:</p> <ol> <li>Download the experiment zip file you wish to run (<code>single_parameter_experiment.zip</code> or <code>multi_parameter_experiment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd single_parameter_experiment</code>).</li> <li>Run <code>poetry install --only main</code> to install all dependencies.</li> <li>To run the experiment, run <code>poetry run python run_experiment.py</code>.</li> <li>All results can be found in the folder <code>data/</code>.</li> </ol> <p>The modules used for the experiment are defined in the file <code>experiment_modules.py</code> and to see the experiment configuration, look in <code>run_experiment.py</code>.</p> <p><strong>Warning</strong>: the experiments take several weeks to run on a single machine, therefore it is advisable to split the experiments based on modules and run them in parallel.</p> <h1>Running the analysis</h1> <p>Prerequisites for running the analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the analysis:</p> <ol> <li>Download the analysis zip file (<code>analysis-adaptive-parameter-control.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd analysis-adaptive-parameter-control</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>If you want to re-run the Bayesian models locally on your machine, follow the <strong>optional</strong> step below, otherwise download and unzip the trace data from the replication package, i.e., <code>Trace data single.zip</code> and <code>Trace data multi.zip</code>. </li> <li>Place the <code>.nc</code> files in the corresponding folder: <code>analysis-adaptive-parameter-control/single_parameter/</code> or <code>analysis-adaptive-parameter-control/multi_parameter/</code>. <ol> <li>E.g. the <code>coverage_rate_model_single_parameter.nc</code> goes in the <code>single_parameter</code> folder, while the <code>coverage_rate_model_multi_parameter.nc</code> goes in the <code>multi_parameter</code> folder.</li> </ol> </li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Data analysis" and run all cells in order.</li> </ol> <h3>(Optional) Running the Bayesian models locally before the analysis.</h3> <ol> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Model specification" and run the three notebook cells.</li> </ol> <p><strong>Warning</strong>: this will take a long time, if you don't have the time, use the following alternative instead</p> <h1>Data</h1> <p>The data from when we ran the experiments is available in the <code>Single data.zip</code> and <code>Multi data.zip</code> files.</p> <p>The structure of these are the following:</p> <ul> <li>There are folders for each module the experiment was run on, further divided into each unique run. All these folders include: <ul> <li>Coverage reports.</li> <li>Complete logs for the unique run.</li> <li>A timeline over controlled parameter values during the test generation process.</li> <li>The complete Pynguin configuration for the run.</li> <li>The generated test suite.</li> </ul> </li> <li>There is one <code>statistics.csv</code> file containing some information about each run and their branch coverage timelines.</li> </ul> <h1>Running the parameter assignment analysis</h1> <p>Prerequisites for running the parameter assignment analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the parameter assignment analysis:</p> <ol> <li>Download the analysis zip file (<code>parameter-assignment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd parameter-assignment</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open the notebook <code>parameter_assignment_analysis.ipynb</code>.</li> <li>Run all cells in order.</li> </ol>
Data from: Comparative productivity of six bioenergy cropping systems on marginal lands in the Great Lakes Region, United States
<p>Growing lignocellulosic crops on marginal lands is a promising solution for sustainable biofuel production. We evaluated the productivity of bioenergy cropping systems (switchgrass [<em>Panicum</em> <em>virgatum</em> L., var. Cave‐In‐Rock], miscanthus [<em>Miscanthus</em> × <em>giganteus</em>, 'Illinois clone'], hybrid poplar [<em>Populus</em> <em>nigra</em> × <em>P. maximowiczii</em> A. Henry 'NM6'], native grasses [five species], early successional vegetation, and restored prairie vs. historical vegetation [as reference control]) with and without nitrogen fertilization on low‐fertility former cropland at five sites in the Great Lakes Region, United States. We reported biomass yields for the first 7 years after establishment. Switchgrass was most consistently productive across all sites, but miscanthus was more productive at three of the five sites. When averaged across sites, years, and nitrogen (N) treatments, biomass yields followed the order miscanthus > switchgrass > hybrid poplar ≈ native grasses > restored prairie > early successional vegetation ≈ historical vegetation, but varied substantially by crop and site, with a significant crop by site interaction. Yields of miscanthus and switchgrass peaked after four to five growing seasons and declined thereafter, while yields of both native grasses and restored prairie increased throughout 6 years with no sign of follow‐on decline, suggesting that polycultures may outperform monocultures over the long term. Yields of early successional vegetation—similar in composition to historical vegetation at each site—did not improve with time. Nitrogen fertilization increased the yields of all cropping systems at all sites. Our results demonstrate the viability of low‐productivity former cropland for long‐term bioenergy production and suggest there is no single crop best suited for all low-fertility soils.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.