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3,172 results for “publications”
City of Seattle, Seattle Public Utilities, Bull Trout Fry Emergence Trapping 2023-current, Cedar River Municipal Watershed, King County, WA
Chester Morse Lake is managed for drinking water supply for the City of Seattle by Seattle Public Utilities (SPU). The reservoir was created in 1915 upon the completion of Masonry Dam, which raised the natural level of Cedar Lake from 1,538 feet to normal operational levels between 1,550 and 1,554 feet, with a maximum refill level of 1,565 feet. Construction of the dam removed several miles of potential stream spawning, incubation, and rearing habitat for an adfluvial population of bull trout, a species listed as threatened under the U.S. Endangered Species Act. The reservoir fluctuates widely across operational elevations during fall and winter storms. During reservoir refill in springtime, however, the reservoir will be continuously filled until reaching peak elevation (1,560 -1,565 feet) terminally inundating habitat where bull trout had previously spawned and embryos and alevins continue to develop in the streambed. Embryo incubation and fry emergence timing data are required to improve SPU's understanding of frequency and magnitude of operational impacts to bull trout embryos developing in stream habitats affected by reservoir inundation. This information enables empirically-based estimates for the number of embryos in the streambed vulnerable to impacts of inundation during fall and winter storms, which is an intermittent impact period for embryos; and reservoir refill, which is a terminal impact period for embryos. Between two and six hand-driven redd egg pocket water quality monitoring wells and quarantine fences (to avoid additional spawning and superimposition) were deployed near primary egg pockets the week of redd observation. Temperature and dissolved oxygen data were recorded in wells and weekly logger downloads informed biologists of incubation conditions (temperature and oxygen) and accumulated temperature units (ATU). Upon reaching approximately 500 ATU, fry emergence traps were deployed over quarantined redds and subsequently, cod ends were sampled
City of Seattle, Seattle Public Utilities, Marbled Murrelet Habitat Enhancement Experiment 2010, Cedar River Municipal Watershed, King County, WA
This experimental project aims to enhance nesting habitat for the marbled murrelet through active habitat restoration in second-growth forests. The project was conducted within the Cedar River Municipal Watershed (CRMW) in Washington State, with the goal of determining if silvicultural treatments, such as creating canopy gaps and tree topping, can accelerate the growth of tree branches suitable for murrelet nesting. The project was implemented in 2010 at a 75-acre site within CRMW. Treatments included removing surrounding trees to increase canopy openness ("gaps"), topping trees to stimulate branch growth, and combining both methods. The site was specifically chosen for its proximity to the murrelet detections in nearby old growth stands, and site suitability in terms of tree age, species composition, and manageable topography. Data collected focused on tree growth and structure characteristics critical to murrelet nesting. A total of 48 trees received treatments, which were systematically compared to untreated controls to assess outcomes. The initial implementation confirmed logistical feasibility and budget adherence, with plans for monitoring and resampling established for the 2020s. If successful, these techniques could be replicated across various environmental conditions to expand viable nesting habitat for the marbled murrelet, directly supporting conservation objectives outlined in the CRMW Habitat Conservation Plan.
City of Seattle, Seattle Public Utilities, Delta Plant Communities 1988-2007, Cedar River Municipal Watershed, King County, WA
Seattle Public Utilities manages the Cedar River Municipal Watershed and reservoir, Chester Morse Lake, to provide drinking water for 1.6 million residents in the greater Seattle area. The Cedar and Rex rivers are the two largest tributaries to Chester Morse Lake and flow over broad, low-gradient deltas. The deltas have mostly fine sediments, sinuous low-flow channels, and an extensive wetland complex with aquatic, herbaceous, shrub, and forest components. Delta plant communities were mapped in 1988, 1996, and 2007 using aerial photography. Plant communities were ground-truthed and boundaries and classification of polygons were corrected where errors were evident. Plant communities were classified into major structural classes, including herbaceous, shrub, deciduous forest, mixed deciduous/conifer forest, and conifer forest. A system of permanent plots was established on the Cedar and Rex river deltas and measured in 1988, 1996, and 2007. Transects comprised of sample plots every 25 meters were surveyed for herbaceous and shrub cover. An additional transect was established in the floodplain of the Cedar River through mixed deciduous and conifer forest to measure tree diameter at breast height and species. This package is complete, and the data were analyzed to evaluate the potential for future adverse impacts to delta plant communities resulting from changes to the reservoir operating regime.
Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </p>
Austrian Science Fund (FWF) Publication Cost Data 2014
<p>Following 2013 (http://dx.doi.org/10.6084/m9.figshare.988754), the Austrian Science Fund (FWF) makes its publication costs spent in 2014 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p> <p>In addition to 2013, this dataset includes also costs for Open Access books and other venues.</p>
Public metagenome datasets annotated using SingleM
<p>These data underlie the community profiles shown at <a href="https://sandpiper.qut.edu.au">https://sandpiper.qut.edu.au</a></p> <p> </p> <h2>Changelog</h2> <p>version 1.0.0</p> <ul> <li>Public metagenomes published before Feb 20, 2025 were analysed using SingleM pipe v0.18.3 (the default R220 metapackage), and then renewed using an R226 metapackage (S5.4.0.GTDB_r226.metapackage_20250331).</li> </ul> <p>version 0.3.0</p> <ul> <li>Update profiles to use GTDB R220, generated using SingleM renew v0.17.0.</li> </ul> <p>version 0.2.0</p> <ul> <li>Initial version. Created using a GTDB R214-based reference SingleM metapackage S3.2.1.GTDB_r214.metapackage_20231006 based on public datasets available Dec 15, 2021.</li> </ul>
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
Public sequence accessions from INSDC, COG-UK and CNCB and EPI_SET from GISAID for SARS-CoV-2 genome sequences in 2023-08-01 UShER tree
<p>Genome sequences and metadata for the accessions in the .tsv.gz (gzip-compressed tab-separated text) files are freely available from their corresponding sources:</p><ul><li>insdc.accessionNameDate.tsv.gz: INSDC (GenBank, ENA, DDBJ) sequences and metadata may be downloaded using NCBI Datasets: https://www.ncbi.nlm.nih.gov/datasets/taxonomy/2697049/ (7,361,734 accessions used on 2023-08-01)</li><li>cog.accessionNameDate.tsv.gz: COG-UK sequences and metadata may be downloaded from https://cog-uk.s3.climb.ac.uk/phylogenetics/latest (as of publication); most COG-UK sequences have been submitted to ENA and are available from INSDC/NCBI Datasets as well. (724,978 accessions used on 2023-08-01)</li><li>cncb.accessionNameDate.tsv.gz: Sequences and metadata from several databases at the China National Center for Bioinformation (CNCB) may be downloaded from GenBase: https://ngdc.cncb.ac.cn/genbase/ (26,604 accessions used on 2023-08-01)</li></ul><p>GISAID data are subject to restrictions on sharing described in https://gisaid.org/terms-of-use/. Genome sequences and metadata are available to registered GISAID users as part of EPI_SET_231106ax at https://doi.org/10.55876/gis8.231106ax (7,718,061 accessions used on 2023-08-01).</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Dataset Publication for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone"
<p><strong>Abstract</strong>: Stress drop catalog data publication supplement for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone" (Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2023) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a>), obtained from wave form analysis using the spectral decomposition technique. Time duration is 2007 to 2021. Seismic events were taken from the IPOC catalog (Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations.<br><a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a>). Wave forms were obtained from the EIDA/GEOPHONE web page (eida.gfz-potsdam.de/webdc3/ or geofon.gfz-potsdam.de/waveform/)</p> <p><strong>File descriptions</strong>: table columns <br>ID, cls, Lon, Lat, Depth, Magntiude, vssource, fc1, fcbound1, fcbound2, sd<br>------------------<br>explanation<br>ID : origin time<br>cls : event class<br>Lon : longitude <br>Lat : latitude<br>Depth : depth in km<br>Magnitude : magnitude (MA)<br>vssource. : s- wave velocity at the event location<br>fc1. : corner frequency in Hz<br>fcbound1 : lower bound for fc1 from 5% variance reduction test in Hz<br>fcbound2. : upper bound for fc2 from 5% variance reduction test in Hz<br>sd : stress drop estimate in MPa</p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
<p>Contact details:</p> <p>wasim.shoman at chalmers.se </p> <p>waahh7 at gmail com</p> <p><strong>Abstract of the research:</strong></p> <p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p> <p><strong>The methodology:</strong></p> <p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p> <p><strong>Datasets details</strong></p> <p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p> <p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table> <p> </p> <p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Rest</p> </td> <td> <p>A value of ”1” indicates a rest stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Break</p> </td> <td> <p>A value of ”1” indicates a break stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ChaDisKM</p> </td> <td> <p>Charged range within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>ChaEnekWh</p> </td> <td> <p>Charged energy within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>KWh</p> </td> </tr> <tr> <td> <p>MainDTN</p> </td> <td> <p>Number of stopped trucks for the main electrification scenario (15%)</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChE30M</p> </td> <td> <p>Charged energy for all stopped trucks</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>ChERM</p> </td> <td> <p>Charged energy for the trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_R</p> </td> <td> <p>Number of trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChEBM</p> </td> <td> <p>Charged energy for the trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_B</p> </td> <td> <p>Number of trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>X, Y coordinates</p> </td> <td> <p>geometry</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Edge_path_E_road</p> </td> <td> <p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance_from_origin_<br> region_to_E_road</p> </td> <td> <p>Distance from the geometric centre of the origin region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_within_E_<br> road</p> </td> <td> <p>Distance of the shortest edge path between the O-D pair</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_from_E_<br> road_to_destination_<br> region</p> </td> <td> <p>Distance from the geometric centre of the destination region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Total_distance</p> </td> <td> <p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2010</p> </td> <td> <p>Number of trucks that drive between the O-D pair in 2010</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2019</p> </td> <td> <p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2030</p> </td> <td> <p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2010</p> </td> <td> <p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2019</p> </td> <td> <p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2030</p> </td> <td> <p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> </tbody> </table> <p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Node_ID</p> </td> <td> <p>Unique network node ID</p> </td> <td> <p>Integer (6 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_X</p> </td> <td> <p>Longitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>Network_Node_Y</p> </td> <td> <p>Latitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>ETISplus_Zone_ID</p> </td> <td> <p>ID of the NUTS-3 region in which the network node is located</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Country</p> </td> <td> <p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Edge_ID</p> </td> <td> <p>Unique edge ID</p> </td> <td> <p>Integer<br> (7 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Manually_Added</p> </td> <td> <p>Determines whether an edge had been manually added to the network (1) or not (0)</p> </td> <td> <p>Binary-integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance</p> </td> <td> <p>Length of the network edge</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Network_Node_A_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_B_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2019</p> </td> <td> <p>Number of trucks that drive on the edge in 2019 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2030</p> </td> <td> <p>Number of trucks that drive on the edge in 2030 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
Dataset for the publication entitled "An exact system of generation for face-milled hypoid gears with uniform depth taper: application to hypoid gear drives with high gear ratio"
Open the record for dataset details and reuse information.
Dataset for a publication: "A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation"
<p>These data are published as part of the paper: A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation. The structure and organization of the data are outlined in the readme file. </p> <p> </p>
telota/lebenswelten: Publication of XML/TEI-data of 'Adlige und bäuerliche Lebenswelten in den Akten ostpreußischer Gutsarchive'
<p>This dataset contains the XML/TEI-files (documents, indexes, and schema) of two digital editions: 'Lebenswelten, Erfahrungsräume und politische Horizonte der ostpreußischen Adelsfamilie Lehndorff vom 18. bis in das 20. Jahrhundert' and 'Die Spiegelung neuzeitlich-bäuerlicher Lebenswelten in den Akten ostpreußischer Gutsarchive'. </p> <p>The digital editions are published under the joint name '<a href="https://lebenswelten-digital.bbaw.de/" target="_blank" rel="noopener">Adlige und bäuerliche Lebenswelten in den Akten ostpreußischer Gutsarchive</a>'.</p> <p> </p> <p>Note: A previous version of the first digital edition (Lebenswelten Lehndorff) was published on Zenodo with the DOI: <a href="../records/3842854" target="_blank" rel="noopener">10.5281/zenodo.3842854</a> and in the GitHub repository <a href="https://github.com/telota/lebenswelten-lehndorff" target="_blank" rel="noopener">lebenswelten-lehndorff</a>.</p>
Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation"
<p>Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation" (DOI: <a title="" href="https://doi.org/10.48328/tudatalib-1376">https://doi.org/10.48328/tudatalib-1376</a>)</p>
Opinions and Views of the Population of Ukraine: May 2024 (KIIS Omnibus 2024/05) – Data from a nationwide public opinion poll conducted by KIIS in May 2024
"Opinions and Views of the Population of Ukraine" is a regular omnibus survey, conducted by Kyiv International Institute of Sociology (KIIS) among Ukraine's adult population and covering a wide range of topics. The data presented here is a subset of the survey conducted in May 2024 and include KIIS's own research questions. Questions included are: readiness for concessions for peace, views on Ukraine's relationship with Russia, perceptions of the war between Russia and Ukraine, views on security agreements, perceptions of Ukrainian society's unity, attitudes toward criticism of the government, attitudes toward the legalization of medical cannabis, and perceptions of Ukraine's statehood during the Soviet era. Data collection took place from May 16 to 22, 2024, with 1,067 respondents interviewed. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results as well as the relevant parts of the original questionnaire.
Supplementary Datasets for the publication "Rousettus aegyptiacus Fruit Bats Do Not Support Productive Replication of Cedar Virus upon Experimental Challenge"
<p>Cedar henipavirus (CedV), which was isolated from the urine of pteropodid bats in Australia, belongs to the genus Henipavirus in the family of Paramyxoviridae. It is closely related to the Hendra virus (HeV) and Nipah virus (NiV), which have been classified at the highest biosafety level (BSL4) due to their high pathogenicity for humans. Meanwhile, CedV is apathogenic for humans and animals. As such, it is often used as a model virus for the highly pathogenic henipaviruses HeV and NiV. In this study, we challenged eight Rousettus aegyptiacus fruit bats of different age groups with CedV in order to assess their age-dependent susceptibility to a CedV infection. Upon intranasal inoculation, none of the animals developed clinical signs, and only trace amounts of viral RNA were detectable at 2 days post-inoculation in the upper respiratory tract and the kidney as well as in oral and anal swab samples. Continuous monitoring of the body temperature and locomotion activity of four animals, however, indicated minor alterations in the challenged animals, which would have remained unnoticed otherwise.</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.