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527 results for “facilities”
Landscape of fear and safety summer 2025 data from University of Michigan Biological Station stream research facilities
Predator prey interactions are often driven by sensory cues and these cues play a role in non-consumptive effects. We are interested in the role that chemical cues (from predators) play in resource use by one of fish common prey, crayfish. We created flow through mesocosms and populated them with crayfish and various configurations of shelters and food. Then we presented to the crayfish predator cues (from large mouth bass) and measured behavioral responses from midnight to 4 am.
Laboratory measurements of wind, waves, and turbulence in hurricane conditions in the ASIST wind-wave facility
<p>Laboratory measurements of wind, waves, and turbulence in hurricane conditions, collected in September and October of 2018 and January of 2019 in the ASIST wind-wave facility, in the SUSTAIN laboratory at the University of Miami.</p> <p>This dataset includes two experiments, one with fresh water ("fresh") and another with seawater ("salt"), each in 10-m winds from 0 to approximately 42 m/s. Data include:</p> <ul> <li>3-dimensional wind velocity at 20 Hz sampling frequency from Campbell Scientific IRGASON sonic anemometer (collected in 2018)</li> <li>2-dimensional (along-tank and vertical) wind velocity at 1000 Hz sampling frequency from TSI IFA-300 hot film anemometer (collected in 2018)</li> <li>1-dimensional (along-tank) wind velocity at 10 Hz sampling frequency from a pitot anemometer (collected in 2018 and 2019)</li> <li>3-dimensional water velocity in the bottom 5 cm of the tank at 100 Hz sampling velocity from Nortek Vectrino velocimeter. (collected in 2018)</li> <li>Water elevation at 20 Hz sampling frequency at 6 locations in the tank from Senix Toughsonic 30 ultrasonic distance meters (collected in 2019)</li> <li>Along-tank static air pressure difference at 10 Hz sampling frequency from Baratron MKS 226 differential pressure transducer (collected in 2019)</li> </ul> <p>All data is in NetCDF4 format.</p> <p>Experiment set up and positions of instruments are documented in more detail in Curcic and Haus (2020), Revised estimates of ocean surface drag in strong winds, <em>Geophysical Research Letters</em>, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647</a>.</p> <p>Produced as part of the National Science Foundation Award #1745384, titled "Air-Sea Momentum Transfer in Extreme Wind Conditions"<strong>.</strong></p> <p>Contact: Milan Curcic <mcurcic@miami.edu></p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities
<p>Example <strong>Microscopy Metadata </strong>(Microscope.JSON and Settings.JSON)<strong> files </strong>produced using<strong> <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> </strong>to document the <strong>Hardware Specifications</strong> of example Microscopes and the <strong>Image Acquisition Settings</strong> utilized to acquire example images as listed in the table below.</p> <blockquote> <p>For each facility, the dataset contains two JSON files:</p> <ol> <li><strong>Microscope.JSON file</strong> (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)</li> <li><strong>Settings.JSON file</strong> (indicated with the name of the image and with the _AS suffix)</li> </ol> </blockquote> <p><strong>Micro-Meta App was</strong> developed as part of a <strong>global community initiative</strong> including the <a href="http://www.4dnucleome.org/"><strong>4D Nucleome (4DN)</strong> </a>Imaging Working Group, <strong>BioImaging North America (BINA)</strong> <a href="https://www.bioimagingna.org/qc-dm-wg">Quality Control and Data Management Working Group</a>, and <strong>QUAlity and REProducibility for Instrument and Images in Light Microscopy</strong> (<a href="https://quarep.org/"><strong>QUAREP-LiMi</strong></a>), to extend the <strong>Open Microscopy Environment (OME)</strong> <a href="https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome.html">data model</a>.</p> <blockquote> <p>The works of this <strong>global community effort</strong> resulted in multiple publications featured on a recent <strong>Nature Methods FOCUS ISSUE </strong>dedicated to <a href="https://www.nature.com/collections/djiciihhjh">Reporting and reproducibility in microscopy</a>.</p> </blockquote> <blockquote> <p><strong>Learn More!</strong> For a thorough description of <strong>Micro-Meta App</strong> consult our recent <a href="https://doi.org/10.1038/s41592-021-01315-z">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.05.31.446382">BioRxiv.org</a> publications!</p> </blockquote> <p> </p> <table> <tbody> <tr> <td><strong>Nr.</strong></td> <td><strong>Manufacturer</strong></td> <td><strong>Model</strong></td> <td><strong>Tier</strong></td> <td><strong>Εxperiment Type</strong></td> <td><strong>Facility Name</strong></td> <td><strong>Department and Institution</strong></td> <td><strong>URL</strong></td> <td><strong>References</strong></td> </tr> <tr> <td>1</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with LSM 710 scan head)</strong></td> <td>1</td> <td>3D visualization of superhydrophobic polymer-nanoparticles</td> <td>Centre for Cell Imaging (CCI)</td> <td>University of Liverpool</td> <td>https://cci.liv.ac.uk/equipment_710.html</td> <td>Upton et al., 2020</td> </tr> <tr> <td>2</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer (Axiovert 200M)</strong></td> <td>2</td> <td>Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP</td> <td>Advanced BioImaging Facility (ABIF).</td> <td>McGill University</td> <td>https://www.mcgill.ca/abif/equipment/axiovert-1</td> <td>Kiepas et al., 2020</td> </tr> <tr> <td>3</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with Spinning Disk)</strong></td> <td>2</td> <td>Immunofluorescence imaging of cryosection of Mouse kidney</td> <td>Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)</td> <td>Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal</td> <td>https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)</td> <td>Pilliod et al., 2020</td> </tr> <tr> <td>4</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Imager Z2 (with Apotome)</strong></td> <td>2</td> <td>Immunofluorescence imaging of mitotic division in Hela cells using </td> <td>Bioimaging Unit</td> <td>Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/</td> <td>Watson et al., 2020</td> </tr> <tr> <td>5</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1</strong></td> <td>2</td> <td>Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.</td> <td>Life Imaging Center (LIC)</td> <td>Centre for Integrative Signalling Analysis (CISA), University of Freiburg</td> <td>https://miap.eu/equipments/sd-i-abl/</td> <td>Hannibal et al., 2020</td> </tr> <tr> <td>6</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI6000B</strong></td> <td>2</td> <td>3D immunofluorescence imaging rhinovirus infected macrophages </td> <td>IMAG'IC Confocal Microscopy Facility</td> <td>Institut Cochin, CNRS, INSERM, Université de Paris</td> <td>https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view</td> <td>Jubrail et al., 2020</td> </tr> <tr> <td>7</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DM5500B</strong></td> <td>2</td> <td>Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks</td> <td>Bioimaging Unit</td> <td>Edwardson Building on the Campus for Ageing and Vitality, Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview</td> <td>da Silva et al., 2019; Nelson et al., 2012<br> </td> </tr> <tr> <td>8</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI8-CS (with TCS SP8 STED 3X)</strong></td> <td>2</td> <td>Live-cell imaging of N. benthamiana leaves cells-derived protoplasts</td> <td>Center for Advanced Imaging (CAi)</td> <td>School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf</td> <td>https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x</td> <td>Singer et al., 2017; Hänsch et al., 2020</td> </tr> <tr> <td>9</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti</strong></td> <td>2</td> <td>Immunofluorescence analysis of the cytoskeleton structure in COS cells</td> <td>Advanced Imaging Center (AIC)</td> <td>Janelia Research Campus, Howard Hughes Medical Institute</td> <td>https://www.janelia.org/support-team/light-microscopy/equipment</td> <td>Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020</td> </tr> <tr> <td>10</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti-E (HCA)</strong></td> <td>2</td> <td>Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies</td> <td>Light Microscopy Facility (IALS-LIF)</td> <td>Institute for Applied Life Sciences, University of Massachusetts at Amherst</td> <td>https://www.umass.edu/ials/light-microscopy</td> <td>Fernandez et al., 2020</td> </tr> <tr> <td>11</td> <td><strong>Nikon Instruments/Coleman laboratory (customized)</strong></td> <td><strong>TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti</strong></td> <td>2</td> <td>Single-particle tracking of Halo-tagged PCNA in Lox cells</td> <td>Coleman laboratory</td> <td>Anatomy and Structural Biology Department, The Albert Einstein College of Medicine</td> <td>https://einsteinmed.org/faculty/12252/robert-coleman/</td> <td>Drosopoulos et al., 2020</td> </tr> <tr> <td>12</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti (with Andor Dragon Fly Spinning Disk)</strong></td> <td>2</td> <td>Investigation of the 3D structure of cerebral organoids</td> <td>Montpellier Resources Imagerie</td> <td>Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier</td> <td>https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html</td> <td>Ayala-Nunez et al., 2019</td> </tr> <tr> <td>13</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium </td> <td>Neuroscience Center Microscopy Core</td> <td>Neuroscience Center, University of North Carolina</td> <td>https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/</td> <td>Aghajanian et al., 2021</td> </tr> <tr> <td>14</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Live-cell imaging of bacterial cells expressing GFP-PopZ</td> <td>Microscopy Resources on the North Quad (MicRoN)</td> <td>Harvard Medical School </td> <td>https://micron.hms.harvard.edu/</td> <td>Lim and Bernhardt 2019; Lim et al., 2019</td> </tr> <tr> <td>15</td> <td><strong>Olympus/Biomedical Imaging Group (customized)</strong></td> <td><strong>TIRF Epifluorescence Structured light Microscope (TESM)/IX71</strong></td> <td>3</td> <td>3D distribution of HIV-1 in the nucleus of human cells</td> <td>Biomedical Imaging Group</td> <td>Program in Molecular Medicine, University of Massachusetts Medical School</td> <td>https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope</td> <td>Navaroli et al., 2012</td> </tr> <tr> <td>16</td> <td><strong>Olympus/Computer Vision Laboratory (customized)</strong></td> <td><strong>3D BrightField Scanner/IX71</strong></td> <td>3</td> <td>Transmitted light brightfield visualization of swimming spermatocytes</td> <td>Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology</td> <td>Universidad Nacional Autonoma de Mexico (UNAM)</td> <td>https://lnma.unam.mx/wp/</td> <td>Pimentel et al., 2012; Silva-Villalobos et al., 2014</td> </tr> </tbody> </table> <p><strong>Getting started</strong></p> <p>Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:</p> <ol> <li><a href="https://vimeo.com/562022222">Video 1</a></li> <li><a href="https://vimeo.com/562022281">Video 2</a></li> </ol> <p><strong>More information</strong></p> <blockquote> <p>For full information on how to use Micro-Meta App please utilize the following resources:</p> <ol> <li>Micro-Meta App <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">website</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/index.html">Full documentation</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/intro/installation.html">Installation</a> instructions</li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/index.html#step-by-step-instructions">Step-by-Step Instructions</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/VideoTutorials.html#micro-meta-app-video-tutorials">Tutorial Videos</a></li> </ol> </blockquote> <p><strong>Background</strong></p> <p>If you want to learn more about the importance of <strong>metadata and quality contro</strong>l to ensure full <strong>reproducibility, quality and scientific value</strong> in light microscopy, please take a look at our recent publications describing the development of community-driven light <strong>4DN-BINA-OME Microscopy Metadata</strong> specifications <a href="https://doi.org/10.1038/s41592-021-01327-9">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.04.25.441198">BioRxiv.org</a> and our <a href="https://arxiv.org/abs/1910.11370">overview manuscript</a> entitled <strong>A perspective on Microscopy Metadata: data provenance and quality control</strong>.</p> <p> </p> <p> </p>
Reuse streams at UK waste facilities (HWRCs)
<div> <p>The UK recycles thousands of working and repairable devices every week at its waste facilities. But fixing or reusing them instead could reduce waste, lower emissions and save households money. </p> <p>Through a citizen science project, we investigated what reuse streams are currently available at household waste and recycling centres (HWRCs) and the challenges of making reuse more widely available.</p> <p>This dataset contains records for all HWRCs across the UK and details of any publicly-communicated reuse streams at each site. It is accompanied by a narrative report. Last updated October 2024.</p> </div>
Fish Facilities Salvage Data, San Francisco, California, 1993-2024
The Bureau of Reclamation’s (Reclamation) Central Valley Project (CVP) and the California Department of Water Resource’s (DWR) State Water Project (SWP) deliver millions of acre-feet of water annually for agricultural, municipal, industrial, and environmental needs in California from the Sacramento-San Joaquin River Delta (Delta) through the C.W. “Bill” Jones Pumping Plant (JPP) and Banks Pumping Plant. Since 1957, a Reclamation operated fish salvage facility, the Tracy Fish Collection Facility (TFCF), is located upstream of the JPP and functions to salvage fish entrained in exported water (Bates and Vinsonhaler 1957). Additionally, the John E. Skinner Delta Fish Protective Facility (SDFPF) was added by the California Department of Water Resources in 1968 to salvage fishes diverted by the SWP. The salvage process involves fish collection, counting, handling and transportation back to the Delta. To prevent fish loss, CVP and SWP fish facilities were constructed at Old river, where fish are diverted to the fish facilities for counting and reporting. After counting, the fish are collected in storage tanks and taken to several release locations in the Delta via fish-haul tank trucks. Millions of fishes from 69 species are salvaged through this process by the two fish facilities. Data collected includes species collected, fish lengths, water flow, export amounts, and water quality. Fishes listed (Endangered Species Act (ESA) or California Endangered Species Act (CESA)) were Chinook Salmon, Delta Smelt, steelhead trout, Longfin Smelt, and Green Sturgeon. The California Department of Fish and Wildlife monitors fish salvage and manages the data collected from the fish facilities in addition to data collected and managed by DWR and Reclamation.
SMART Infrastructure Facility Building Data
<p><strong>SMART Building Data</strong></p> <p>Authors: J. Barthelemy, B. Arshard, N. Verstaevel, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 08 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet sensors installed in every room of the SMART Infrastructure Facility of the University of Wollongong. The sampling rate is set to one minute and the data is transmitted via a LoRaWAN network.</p> <p>Each device sense temperature, humidity, luminosity, pressure, movement and CO2 (*). In addition, they transmit their orientation (*), node id, room id and battery voltage. Each packet received by the LoRaWAN network is also characterized by a RSSI, an SNR and a checksum. The (*) CO2 and orientation data are not accurate.</p> <p><strong>Data dictionary</strong></p> <p>The dataset contains the following features:</p> <p>* info : the type of information for the current row. It can be: <br> - temp -> temperature (Celcius)<br> - humidity -> humidity (%)<br> - light -> luminosity (1024 levels, 0 being the darkest and 1024 the brightest)<br> - co2 -> CO2 (1024 levels, 0 being the lowest, 1024 the highest)<br> - pressure -> pressure (hPa)<br> - orient -> orientation of the sensor<br> - nodeId -> internal id of the sensor<br> - roomNum -> room id of the sensor<br> - voltage -> battery voltage of the sensor (V)<br> - movement -> motion detection (True/False)<br> - checksum -> checksum of the packet transmitted via LoRaWAN<br> * room_id : the room id in which the sensor is installed (see map of the building)<br> * date_time : the timestamp of the data<br> * bool_v : boolean value (true/false) for movement<br> * str_v : string value for nodeId, checksum, roomNum<br> * long_v : integer (long) value for light, orient, rssi, co2, humidity<br> * dbl_v : real (double) value for voltage, pressure, temp, snr</p> <p><strong>Notes</strong></p> <p>- The uncompressed dataset is a 40Gb CSV file.<br> - Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf.<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/.</p>
Anomaly detection in the Zwicky Transient Facility DR3
<p>The feature data set extracted from <a href="https://www.ztf.caltech.edu/page/dr3">ZTF DR3</a> light curves. It was used in <a href="https://arxiv.org/abs/2012.01419">Malanchev et al. 2020</a> to detect anomalous astrophysical sources in ZTF data. </p> <p>"feature_XXX.dat" files contain object-ordered light curve feature data, every object is built on 42 feature values, which are encoded as little endian single precision IEEE-754 float (32bit float) numbers. Feature code-names are the same for all three data sets and are listed in plain text files "feature_XXX.name", one code-name per line. "oid_XXX.dat" files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers. "oid_XXX.dat" and "feature_XXX.dat" have same object order, for example the first 8 bytes of "oid_m31.dat" files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of "feature_m31.dat" file. "m31", "deep" and "disk" denote different ZTF fields and contain 57 546, 406 611, 1 790 565 objects. Note that observations between 58194 ≤ MJD ≤ 58483 are used, see <a href="https://doi.org/10.1093/mnras/stab316">the paper</a> for field and features details.</p> <p>The sample Python code to access the data as Numpy arrays:</p> <pre><code class="language-python">import numpy as np oid = np.memmap('oid_m31.dat', mode='r', dtype=np.uint64) with open('feature_m31.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_m31.dat', mode='r', dtype=dtype, shape=oid.shape) idx = np.argmax(feature['amplitude']) print('Object {} has maximum amplitude {:.3f}'.format(oid[idx], feature['amplitude'][idx]))</code></pre> <p> </p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Snail Shell Strength and Total Crush Force of a Northern Michigan Snail as a Function of Predation Risk at the University of Michigan Biological Station Stream Research Facility (5/31/23-8/1/23)
Many prey organisms respond to the non-consumptive effects of predators by altering their physiology, morphology, and behavior. These inducible defenses can create refuges for prey by decreasing the likelihood of consumption by predators. Some prey, as in marine mollusks, have been shown to alter their morphology in response to the presence of size-limited predation. To extend this work into the freshwater realm, we presented pointed campeloma snails (Campeloma decisum) to chemical cues from a natural predator, the rusty crayfish (Faxonius rusticus), to better understand how snail morphology changes under the threat of predation. The total force needed to crush shells, total shell length, aperture width, and total weight, along with changes to these three body measurements were recorded for each individual and used to quantify morphological changes as a function of risk. Snails exposed to crayfish chemical cues needed significantly more force to crush their shells than controls (p = 0.002). Total shell length was greater in crayfish exposed snails than control snails (p = 0.002), and snails in the crayfish treatment also showed significantly more change in shell length than control snails (p = 0.003). Similarly, aperture width was significantly greater in exposed snails (p = 0.002). However, exposed snails exhibited significantly less change in aperture width than controls (p = 0.017). Finally, we found that snails exposed to crayfish weighed significantly more than snails in the control (p = 0.0009). Thus, the results of this study show that morphology of gastropods is altered in the presence of predators, and this may be an antipredator tactic directly related to risk.
Hohokam canals as multi-use facilities: Pre-historic canal system in the central Arizona-Phoenix metropolitan area
This is the digitized version of a map of the Hohokam canal system in what is now the Phoenix metropolitan area. It is based on the thesis research by J. B. Howard (Howard, J. (1990). Paleohydraulics : techniques for modeling the operation and growth of prehistoric canal systems. Thesis (M.A.)--Arizona State University, 1990). The original paper map is based on previous archaeological data, overlayed onto USGS 7.5 minute quadrangle maps to recreate the canal pattern.
PIE LTER, geographic information for the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA.
A description of the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA, USA. The marsh around Clubhead Creek, Rolwey, MA, USA was used as a reference.
X-ray diffraction images of bovine trypsin crystals recorded at the FemtoMAX beamline of Max IV synchrotron facility
<p>The deposition concerns bovine trypsin diffraction images in two wedges. Each image is recorded on a still crystal and separated by 0.1 deg rotation. The x4.tar.gz archive contains summed intensities from individual snapshots at the same orientation, whereas x4_single.tar.gz archive contains single snapshots/orientation. </p>
Destination Choice Model including panel datausing WiFi localization in a pedestrian facility
<p>A minimal example of a destination choice model including panel data on EPFL campus. It is based on the output of Danalet<em> et al. </em>(2014)<em>.</em> </p> <p>It runs on Pythonbiogeme. Some variables are removed from the dataset due to privacy issues. Thus, some parameters may not be significant.</p>
Data licences and organization type of contributors to the Global Biodiversity Information Facility as of 19 January 2016
<p>Data from the Global Biodiversity Information Facility were extracted using R (version 3.2.0) on 9 July 2015 using the rgbif package (version 0.9.0) (Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2015) Package ‘rgbif’: Interface to the Global 'Biodiversity' Information Facility 'API' http://cran.r-project.org/web/packages/rgbif/rgbif.pdf). The ‘rights’ statements was extracted for all occurrence datasets with one or more observations. A total of 12,458 datasets were extracted, but only about 11% of the datasets have an explicit data-useage-rights statement at the dataset level. However, some datasets use the occurrence level ‘rights’ and ‘accessRights’ fields. To extract these data the rights information was obtained from the first record of each dataset where a rights statement was missing at the dataset level.</p> <p>The datasets were categorized into 13 different types depending on the origin of the observations.</p> <ol> <li>Biodiversity Information Facility or data centre</li> <li>Botanical Garden or Herbarium</li> <li>Citizen science</li> <li>Commercial</li> <li>Data publisher</li> <li>Educational</li> <li>Government</li> <li>Museum</li> <li>Network</li> <li>Parks Authority or Nature Reserve</li> <li>Research institution</li> <li>Society</li> <li>Foundations</li> </ol>
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Supplementary Data
<p><strong>Videos</strong></p><ul><li><strong>Video 1</strong> A video going through the Z stack in single slices. This is a cross- sectional view of the XRH image stack along the XY plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 2 </strong>A video going through the Y stack in single slices. This is a cross- sectional view of the XRH image stack along the XZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 3 </strong>A video going through the X stack in single slices. This is a cross- sectional view of the XRH image stack along the YZ plane. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 4 </strong>3D X-ray histology (XRH) is a µCT -based workflow tailored to fit seamlessly into current histology workflows in biomedical and pre-clinical research, as well as clinical histopathology. Microanatomical detail can be captured from standard (non-stained) formalin-fixed and paraffin-embedded (FFPE) tissue blocks.</li><li><strong>Video 5</strong> Average Intensity Projection (AIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Average Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 6 </strong>Maximum Intensity Projection (MIP) of the sample through the Histologically relevant plane. This is a 2D visualisation rendering the Maximum Intensity of 20x single XY slices along the z-axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li><li><strong>Video 7 </strong>Standard deviation projection of the sample going through the histologically relevant plane. This is a 2D visualisation rendering the Standard Deviation of 20x single XY slices along the z- axis of the stack. XRH datasets are normally oriented (resliced) in a way that a scroll through the stack along the XY plane emulates the physical histology slicing of the tissue.</li></ul><p><i>* <strong>Videos 5 -7</strong> are also referred to as "thick-slice rolls" </i>- <i>Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack.</i><br> </p><p><strong>The questionnaire used to collect feedback about the needs of the XRH community.</strong></p><ul><li>Survey.docx</li><li>Survey.pdf</li></ul><p><br><strong>Exemplar report of a semi-automatically generated augmented PDF file</strong> that contain sample information, imaging settings, still images with descriptive figure legends, and links to corresponding online videos</p><ul><li>DEMO02019-FFPE_report_99EbPXG.pdf</li></ul><p> </p><p>= = = = = = = = = = = = = = = = <br><strong>System performance data ZIP</strong><br>= = = = = = = = = = = = = = = = </p><p>This ZIP file contains imaging data collected through different systems and setups at the XRH facility at the μ-VIS X-ray Imaging Centre at the University of Southampton for the purpose of acceptance and/or system performance characterisation. Below is an overview of the folder structure and its contents</p><p>The following files are X-ray imaging data collected on September 28, 2017, using the Med-X system and a Jima phantom at 55 kV peak and 7 Watts. </p><ul><li>20170928_MEDX_1642_JIMA_55kVp7W-2.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif</li><li>20170928_MEDX_1642_JIMA_55kVp7W.tif.profile.xml</li></ul><p>This PDF document is related to a QRM MicroCT bar pattern phantom, and its specifications</p><ul><li>QRM-MicroCT-Barpattern-Phantom.pdf</li></ul><p>Graphs showing the calculated focal-spot size as a function of the X-ray power (W) for the Molybdenum rotating target calculated using Edge Modulation function testing. The performance is then compared with the performance of the Reflection target across the same range of powers. Raw data can be found in XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize folder. Test performed in July 2021. </p><ul><li>XRH_202107_MoRot-testing_EdgeModFunction-QRMrecons+RotReflCompar.png</li></ul><p> </p><p><i><strong>/ XRH-XT-H-225-ST_FocalSpots</strong></i><br>This directory contains radiographic data collected using the XRH system with a JIMA phantom and MoRt (Molybdenum rotating), TT (Transmission), and Reflection targets.</p><ul><li>20200113_XRH_Jima test MoRT 55kV 15W.tif, 20200113_XRH_Jima test MoRT 55kV 30W.tif, etc.: <br>These files represent radiographs taken on January 13, 2020, using the XRH system, Jima phantom, MoRT target at 55 kVp and varying wattages.</li><li>20200207_XRH_JIMA 80kV TT1a.tif, 20200207_XRH_JIMA 80kV TT1b.tif, etc.<br>Similar to the above, these files are from February 7, 2020, and use 80 kVp with a TT target.</li><li>20231115_XRH_reflW_80kVp6W.tif, 20231115_XRH_reflW_80kVp6W_02.tif, etc.<br>These files are from November 15, 2023, and collected using the XRH system with a Reflection target at 80 kVp and 6 Watts.</li></ul><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleRadioFromCTs_5umPixelSize</strong></i><br>This directory contains single radiographs taken with a pixel size of 5 micrometers using the Molybdenum rotating (MoRt), and the Reflection target using tungsten (W) and Molybdenum (Mo) metals.</p><p><i><strong>/ XRH_QRM_Refl-vs-Rot-TargetComparison_SingleReconSlices_5umPixelSize</strong></i><br>This directory contains sinlge reconstruction slices of the setups mentioned above. Slices are exported from CT volumes and were used for the Edge Modulation function study. </p><p>For interpretation of the filenames in the folders listed above please see below and refer to specific files and folders for detailed information and results related to each imaging session:</p><ul><li><i><xx>kVp or <xx>kV </i>:Imaging at a peak voltage of <xx> kVp.</li><li><i><y>W</i> :Imaging at <y> Watts;<i> </i>"." is represented with "-"; i.e. 20210705_XRH_2766_PJB_TEST03552-EQPMT_W_6-9W is acquired using a power of 6.9 W</li><li><i>MoRt, TT, Refl </i> :Molybdenum, Transmission, and Reflection targets, respectively.</li><li><i>_W_ and _Mo_ </i> :Tungsten and Molybdenum target materials.</li><li><i>_horiz</i> :Reconstruction slices in line with the X-ray beam's propagation direction.</li><li><i>_vert</i> :Reconstruction slices normal to the X-ray beam's propagation direction and parallel to the detector plane.</li></ul>
Raw data for the article entitled "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"
<p>Raw data for the plots in the open access article "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"</p>
Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning
<p><strong>TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW.</strong></p> <p>Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a></p> <p><a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a></p> <h1><strong>1. Dataset Description</strong></h1> <p>We publish six files.</p> <ul> <li><em>analysis_polygons.gpkg:</em> our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li><em>analysis_polygons.csv:</em> a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li><em>sources.csv</em>: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li><em>raw_polygons.gpkg:</em> the raw geometries used to compose analysis_polygons.gpkg.</li> <li><em>TZ Solar Asset Mapper Q1 2024.xlsx</em>: an Excel formatted version of the analysis_polygons.csv file.</li> <li><em>tz-sam_scientific_data.pdf</em>: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2><strong>1.1 Analysis-level datasets</strong></h2> <p>Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets.</p> <p>The geospatial dataset contains the following fields:</p> <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> <p>The CSV version replaces the Geometry column with:</p> <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2><strong>1.2 Raw datasets and sources</strong></h2> <p>The analysis-level datasets hide some complexity in the underlying data that we expose in the <em>raw_polygons</em> and <em>sources</em> file.</p> <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> <p>Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon.</p> <p>raw_polygons.gpkg contains the following fields:</p> <ul> <li>id: ID of the raw source polygon</li> <li>geometry<strong>: </strong>Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <p>Sources.csv contains the following fields:</p> <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2><strong>1.3 Caveats and limitations</strong></h2> <h3><strong>1.3.1 Capacity Estimates</strong></h3> <p>While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%.</p> <h3>1.3.2 Plant Shapes</h3> <p>Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out.</p> <h3>1.3.3 Capacity Updates</h3> <p>Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates.</p> <h3>1.3.4 Construction Date Estimates</h3> <p>We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound.</p> <p>We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates.</p> <p><strong>2. Attribution</strong></p> <p>TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats:</p> <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
Accommodation Facilities and Ratings on Booking for 10 European Cities
<p>This dataset, collected on 11/11/2024, includes data from 4,806 accommodations across 10 European cities: Barcelona, Reykjavík, Amsterdam, Prague, Sofia, Porto, Edinburgh, Berlin, Dubrovnik, and Innsbruck. With prices corresponding to the 16/07/2025 night (2 adults and 1 room), it features global ratings, and category scores like cleanliness, comfort, staff, and location, along with details on available amenities and accommodation types. The dataset also covers check-in/check-out policies, pet policies, exact addresses, and review counts. It is designed to analyze key factors to a positive accommodation experience, and it allows comparisons across cities and types of accommodations.</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.