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21,983 results for “UNITE”
Diversity-Driven Unit Test Generation (Data Set)
<p>The goal of automated unit test generation tools is to create a set of test cases for the software under test that achieve the highest possible coverage for the selected test quality criteria. The most effective approaches for achieving this goal at the present time use meta-heuristic optimization algorithms to search for new test cases using fitness functions defined on existing sets of test<br> cases and the system under test. Regardless of how their search algorithms are controlled, however, all existing approaches focus on the analysis of exactly one implementation, the software under test, to drive their search processes, which is a limitation on the information they have available. In this paper we investigate whether the practical effectiveness of white box unit test generation tools can be increased by giving them access to multiple, diverse implementations of the functionality under test harvested from widely available Open Source software repositories. After presenting a basic implementation of such an approach, DivGen (Diversity-driven Generation), on top of the leading test generation tool for Java (EvoSuite), we assess the performance of DivGen compared to EvoSuite when applied in its traditional, mono-implementation oriented mode (MonoGen). The results show that while DivGen outperforms MonoGen in 33% of the sampled classes for mutation coverage (+16% higher on average), MonoGen outperforms<br> DivGen in 12.4% of the classes for branch coverage (+10% higher average).</p>
United States Flood Database
<p>This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.</p> <p>Description of filenames:</p> <p>1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.</p> <p>Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code</p> <p>2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity</p> <p>3. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx?versionId=b7c2ba8d-1aad-4268-9f0b-cdadd95b38c3">emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx</a> – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.</p> <p>Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI</p> <p>4. <a href="https://zenodo.org/api/files/cc1f4627-ccd7-46e2-9d9f-b7d58b456cd2/extracted_events_NOAA.csv?versionId=3c47db6f-f908-4c04-93c4-62afb5f8a68f">extracted_events_NOAA.csv</a> – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.</p> <p>Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE<strong> </strong></p> <p>5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.</p> <p>Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry</p> <p>6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.</p> <p>Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection</p> <p>7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.</p> <p>Data attributes: id, obtime, category, description, description_id, lon, lat</p> <p>8. USFD_v1.1.csv – A merged United States Flood Database from 1900 to the present (UPDATED)</p> <p>Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.</p> <p>Details of attributes:</p> <p>DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS</p> <p>DATE_END: end datetime of an event. yyyymmddHHMMSS</p> <p>DURATION: duration of an event in hours</p> <p>LON: longitude in degrees</p> <p>LAT: latitude in degrees</p> <p>COUNTRY: United States of America</p> <p>STATE: US state name</p> <p>AREA: affected areas in km^2</p> <p>FATALITY: number of fatalities</p> <p>DAMAGE: economic damages in US dollars</p> <p>SEVERITY: event severity, (1/1.5/2) according to DFO.</p> <p>SOURCE: flood information source.</p> <p>CAUSE: flood cause.</p> <p>SOURCE_DB: source database from item 1-7.</p> <p>SOURCE_ID: original ID in the source database.</p> <p>DESCRIPTION: event description</p> <p>SLOPE: calculated slope based on SRTM DEM 90m</p> <p>DEM: Digital Elevation Model</p> <p>LULC: Land Use Land Cover</p> <p>DISTANCE_RIVER: distance to major river network in km,</p> <p>CONT_AREA: contributing area (km^2), from MERIT Hydro</p> <p>DEPTH: 500-yr flood depth</p> <p>YEAR: year of the event.</p> <p>9. attribution_table.xlsx – description of each database, and URLs are provided to retrieve these databases.</p> <p>The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.</p> <p>If you intend to use this dataset, please cite our description paper:</p> <p>Li, Z., Chen, M., Gao, S., Gourley, J. J., Yang, T., Shen, X., Kolar, R., and Hong, Y.: A multi-source 120-year US flood database with a unified common format and public access, Earth Syst. Sci. Data, 13, 3755–3766, https://doi.org/10.5194/essd-13-3755-2021, 2021.</p> <p> </p>
Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"
<p>This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023</p>
Dataset for the calibration of the photosynthetic unit (PSU)-model developed at KIT
<p>The presented dataset was used to develop and calibrate (estimate the model parameters) a photosynthetic unit (PSU)-model. The PSU-model and the data were generated at the Institute of Process Engineering in Life Sciences – Bioprocess Engineering of the KIT under the supervision of Prof. Dr.-Ing. Clemens Posten. Please use this data to familiarize yourself with the PSU-model and as an example dataset. If you want to use your own data in this context, organize it like the sample data listed here. As an alternative, you should adapt the model scripts to your data accordingly.</p> <p>Date of data collection: The data were generated as a part of the author’s PhD program between 2018-2022 at Karlsruhe Institute of Technology (KIT).</p>
Virome diversity of Hyalomma dromedarii ticks collected from camels in the United Arab Emirates
<p>Viruses are important components of the microbiome of ticks. Ticks are capable of transmitting several serious viral diseases to humans and animals. Hitherto, the composition of viral communities in <em>Hyalomma dromedarii</em> ticks associated with camels in the United Arab Emirates (UAE) remains unexplored. The purpose of this study was to characterize the RNA virome diversity in male and female <em>H. dromedarii</em> ticks collected from camels in Al Ain, UAE.<strong> </strong>We collected ticks, extracted and sequenced RNA, using Illumina (NovaSeq 6000) and Oxford Nanopore (MinION).<strong> </strong>From the total generated sequencing reads, 180,559 (~0.35 %) and 197,801 (~0.34 %) reads were identified as virus-related reads in male and female tick samples respectively. Taxonomic assignment of the viral sequencing reads was accomplished based on bioinformatic analyses. Further, viral reads were classified into 39 viral families. Poxiviridae, Phycodnaviridae, Phenuiviridae, Mimiviridae, and Polydnaviridae were the most abundant families in the tick viromes. Notably, we assembled the genomes of three RNA viruses, which were placed by phylogenetic analyses in clades that included the Bole tick virus.<strong> </strong>Overall, this study attempts to elucidate the RNA virome of ticks associated with camels in the UAE and the results obtained from this study improve the knowledge of the diversity of viruses in <em>H. dromedarii</em> ticks.</p>
Data for "Climate change is shifting and narrowing prescribed fire windows in the Western United States"
<p><strong>The data archived here represent two types of information used in the publication “Climate change is shifting and narrowing prescribed fire windows in the Western United States” by Swain et al. 2023:</strong></p> <p>1) The meteorological and vegetation dryness/fuel moisture data (as described within the file) extracted from prescribed fire burn plans around the Western United States (drawn from entities such as the U.S. Forest Service, U.S. National Park Service, and The Nature Conservancy) between 2002 and 2022. This data is provided in tabular form, both as an .xlsx file and a .csv file for the convenience of the user. In addition to the specific values from each burn plan, summaries of median values for forested and non-forested landscapes are provided as well.</p> <p>2) The number of days on which environmental conditions (i.e., weather and vegetation fuel moistures) are acceptable for prescribed fire according to the composite metric described in Swain et al. 2023 (known as “RxDays”). Underlying RxDay definitions are different for forest and non-forested landscapes. This data is provided in geospatially explicit (gridded) form as NetCDF files, which are a self-describing file format. Each file is provided as a single 3-dimensional hypercube (i.e., dimensions of time, latitude, and longitude, respectively; units and details described within the file) corresponding to the number of RxDays per calendar month.</p> <p>One file is provided for each climate model iteration (as identified in each filename); these represent projected RxDays at monthly scale between 1981 and 2060 using an RCP 4.5 climate forcing trajectory. An additional file (rx_gridMET.nc) is provided that represents the same values from an atmospheric reanalysis dataset, which represents a best estimate of observed RxDay values (1981-2020).</p>
Systematic revision of the Spotted and Northern Dusky Salamanders (Plethodontidae: Desmognathus conanti and D. fuscus), with six new species from the eastern United States
<p>Appendix S1 - Specimen, locality, and morphometric data for 674 individual salamanders analyzed in this study. We used the ‘geomorph’ package in R (Adams and Otárola-Castillo 2013) to extract 17 measurements at 0.01mm precision: SVL (snout-vent length), TL (tail length), AG (axilla-groin length), CW (chest width), FL (femur length), HL (humerus length), SG (snout-gular length), TW (tail width at rear of vent), TO (length of third toe), FI (length of third finger), HW (head width), ED (eye diameter), IN (internarial distance), ES (eye-snout distance), ON (orbito-narial distance), IO (inter-orbital distance), and IC (inter-canthal distance).</p>
Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
<p>This repository contains supplementary data for the journal paper:</p> <blockquote> <p>Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi: 10.3389/fnbot.2023.1179224</p> </blockquote> <p>It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive <strong>EMG_model_configs.zip</strong>.</p> <p><br> The files <strong>IP_tracking_opt_res.json</strong><a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and <strong>IP_tracking_opt_res.pkl</strong> contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored.</p> <p>For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.</p> <p> </p> <p>[1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152</p>
Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States
<p><strong>Description</strong></p> <p>This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request. </p> <p> </p> <p><strong>Abstract</strong></p> <p>Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.</p> <p> </p>
Cipher System T310/50, power supply unit (Inv. 1997-15T2)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.</p> <p>object details:<br> name: Cipher System T310/50, power supply unit<br> Inv.-No. of the Deutsches Museum: 1997-15T2</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.668<br> Y pixel spacing: 0.669<br> Z pixel spacing: 0.500<br> grid size x: 1715<br> grid size y: 1626<br> grid size z: 1117<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
Cipher System T310/50, control unit (Inv. 1997-15T3)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.</p> <p>object details:<br> name: Cipher System T310/50, control unit<br> Inv.-No. of the Deutsches Museum: 1997-15T3</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.126<br> Y pixel spacing: 0.126<br> Z pixel spacing: 0.126<br> grid size x: 2415<br> grid size y: 2362<br> grid size z: 1695<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
Cipher Machine Siemens & Halske Mixer Unit One Time Tape_surface meshes (Inv. 2017-449)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Virus operational taxonomic units (vOTUs) from Stordalen Mire, Sweden
<p>This file is a collection of 5,051 virus operational taxonomy units (vOTUs) that were identified from samples collected from Stordalen Mire, a long-term ecological research site in Abisko, Sweden. Peat soil was sampled from the active layer across 3 different thaw stages (palsa, bog, and fen) between 2010-2017. From 379 bulk metagenomes, virus sequences were identified <em>in silico</em> using VirSorter1 (with additional stringent filtering criteria). Virus sequences above 5kb in length were then clustered into vOTUs (using a threshold of 95% identity and 80% coverage). This vOTU dataset is the basis of a research paper, entitled 'Virus ecology and 7-year temporal dynamics across a permafrost thaw gradient,' (Sun and Pratama, <em>et al.</em>, <em>in prep.</em>), which provides insights into permafrost viruses and their role in terrestrial carbon cycling.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</p>
Pond data: physical, chemical, and biological characteristics with scientific and United States of America state definitions from literature and legislative surveys
Ponds are often identified by their small size and shallow depths, but the lack of a universal definition hampers science and weakens legal protection. In order to determine a working definition of ‘pond’, we conducted a literature search for scientific definitions, a U.S. state survey for management definitions, and looked at pond ecosystem function using data from the literature search. Our dataset includes physical, chemical, and biological data for 1327 waterbodies ≤ 20 ha in surface area and ≤ 9 m in maximum or mean depth from our literature review. These data have a global distribution, we include a table of latitudes and longitudes, and span many years (1946-2019). We have also included a table of 54 pond definitions from the literature review and a table of U.S. state definitions of ponds, wetlands, and lakes resulting from our survey.
Southwest United States Wetland Water Quality and Macroinvertebrate Data 2018-2022
Water quality and macroinvertebrate data were collected between 2018 and 2020 from 14 different wetland and riparian sites spanning across West Texas, New Mexico, and Arizona. Water quality data such as Cl, SO4, and conductivity were collected as well as nutrients such as NO3, PO4, and Total Dissolved Nitrogen. Macroinvertebrate data were collected from all sites during the summer months (June, July, and August).
University of Kansas Field Station: Forest demography, 1980 – 2015. On ten study plots established on three management units all live trees with a dbh > 7.5 cm (3 in) were identified to species, measured, and tagged. Trees were initially measured in 1980/1981 and re-measured in three successive time periods: 1993/95; 2002/03; and 2014/15. Trees will be measured again in 2025/26.
In 1980 researchers at the University of Kansas initiated a long-term experiment monitoring the composition of oak-hickory forest communities at the University’s field station near Lawrence, Kansas. The purpose of the study was to determine how forest species composition varied temporally across distinct habitats that varied in topography, elevation, sun exposure, management history and successional stage. Ten permanent sites were sampled approximately each decade with data collection periods of 1980/81, 1993/95, 2002/03, and 2014/15. Trees with a minimum diameter at breast height (dbh) of ≥ 7.5 cm were tagged, identified to species and measured. Trees will be measured again in 2025/26.
LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States
Time series of median summer water clarity (secchi) values from 601 unique lakes in the Midwest and Northeast United States. Water clarity observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1. These data were used to assess long-term changes in water clarity from 1987-2011, and the potential drivers of those trends (Lottig et al. in press). Summer open water period was used to approximate the stratified period in the study lakes, which was defined as June 15 to September 15. Over the 25-year time period, each lake had to have at least a single summer water clarity observation for 22 of 25 years. The median number of secchi measurements that were used to derive a single annual median value for each lake was approximately 9. Of the over 14,000 annual estimates of water clarity that we generated, only two percent of those annual values were generated from a single observation and median number of observations for each lake over the 25-year study period was 223. Each unique lake with water clarity data also has supporting geophysical data, including climate, land use, hydrology, and topography derived at multiple spatial scales. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03 except for the annual climate data which was aggregated at the HUC8 spatial scale from monthly PRISM data. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Citations: Lottig, N.R., P-N. Tan, T. Wager, K.S. Cheruvelil, P.A. Soranno, E.H. Stanley, C.E Scott, C.A. Stow, and S. Yuan. in press. Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., G
Modeled maximum and mean lake depths for the contiguous United States
Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Zmax; n = 16,831) and mean (Zmean; n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Zmax R^2 = 0.35; RMSE = 8.1 m and Zmean R^2 = 0.36; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. Additionally, despite orders of magnitude variation in surface area, most size classes had a modeled modal maximum depth of ~5 m. Concave (bowl-shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel-shaped) as surface area increased. Across the conterminous US, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous US is 1,068 to 1,298 km3, depending on whether Zmax or Zmean was modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern US.
Mapping EDI, NEON and DataONE units to the QUDT ontology, 2022
In the metadata of digital environmental datasets, automated processing is hindered by the wide variety of representations for unit that may be human-readable, but may not be unambiguous or machine-interpretable, (e.g., grams per square meter, gm/m2, g/m2, gm-2, g/m^2, g.m-2, g m-2 and gramPerMeterSquared). Matching disparate representations of the same unit into a single unit concept from an ontology assists with interpretation and reuse by providing a linkage to a complete unit definitions with label, description, dimensions. Datasets with shared units can be identified during searches, and are more suitable for automating analyses and potential transformation. This dataset contains data and code associated with a project to map units in ecological metadata collected between 2013 and 2022 by DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network to the QUDT ontology using successive string transformations. Data entities include a) raw metadata as received (355,057 unit instances) b) integrated raw data c) substitution tables for string transformations d) resulting lookup table for 896 distinct units matched to QUDT units e) associated R code used for QUDT matching plus a web service and R functions for adding annotation elements to Ecological Metadata Language metadata documents. Using these substitutions and code, 91% of unit instances in the raw metadata could be matched to QUDT. Data and results are discussed in “Porter JH, M O’Brien, M Frants, S Earl, M Martin, C Laney. (in review) Using a Units Ontology to Annotate Pre-Existing Metadata. Submitted to Scientific Data.
Analysis of aboveground biomass in longleaf pine forests in southeast United States, 2015 to 2019
I obtained inventory data from the USDA Forest Inventory and Analysis (FIA) program, covering 1999 to 2022, focusing on eight southeastern states in the U.S. (Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, and Louisiana). For this study, I filtered the data for 2015-2019, during which the FIA captures 20% of plots in each state annually. Using R, I converted aboveground biomass (AGB) from pounds to kg/ha, tree height from feet to meters, and diameter at breast height (DBH) from inches to centimeters to align with the metric system. Structural diversity of tree height and diameter was calculated using the Shannon diversity index based on tree height and diameter classes. Climate data from PRISM and soil carbon to nitrogen data from the World Soil Database were integrated with the FIA data using the nearest neighbors method in the SF package in R. The combined dataset was standardized for Structural Equation Modeling (SEM) analysis.
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.