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802 results for “Commercialization”
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Open-source traffic and CO2 emission dataset for commercial aviation
<p>This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. <br>It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. </p> <p>Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>).<br>A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a><br>The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.<br> </p> <h4><br><strong>1- DISCLAIMER</strong></h4> <p><br>The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. </p> <p>Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use.</p> <p>As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance.</p> <h4><br><strong>2- DESCRIPTION</strong></h4> <p>Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.</p> <p><em>Please </em>refer<em> to </em>the<em> support article for more details (see above).</em></p> <p>The dataset contains the following columns:</p> <ul> <li>"First column" : index</li> <li><strong>airline_iata : </strong>IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li><strong>acft_icao : </strong>ICAO code of the aircraft type</li> <li><strong>acft_class : </strong>Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li><strong>seymour_proxy: </strong>Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li><strong>source: </strong>Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li><strong>seats: </strong>Number of seats available for the data entry, AFTER airport residual scaling</li> <li><strong>n_flights: </strong>Number of flights of the data entry, when available</li> <li><strong>iata_departure</strong>, <strong>iata_arrival : </strong>IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li><strong>departure_lon</strong><em>, </em><strong>departure_lat</strong><em>, </em><strong>arrival_lon</strong><em>, </em><strong>arrival_lat : </strong>Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li><strong>departure_country, arrival_country</strong>: Origin and destination country ISO2 code. <strong>WARNING: </strong>disable NA (Namibia) as default NaN at import</li> <li><strong>departure_continent, arrival_continent: </strong>Origin and destination continent code. <strong>WARNING: </strong>disable NA (North America) as default NaN at import</li> <li><strong>seats_no_est_scaling: </strong>Number of seats available for the data entry, BEFORE airport residual scaling</li> <li><strong>distance_km: </strong>Flight distance (km)</li> <li><strong>ask: </strong>Available Seat Kilometres</li> <li><strong>rpk: </strong>Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li><strong>fuel_burn_seymour: </strong>Fuel burn <em>per flight</em> (kg) when seymour proxy available</li> <li><strong>fuel_burn: </strong>Total fuel burn of the data entry (kg)</li> <li><strong>co2: </strong>Total CO2 emissions of the data entry (kg)</li> <li><strong>domestic: </strong>Domestic/international boolean (Domestic=1, International=0)</li> </ul> <p> </p> <h4><strong>3- Citation</strong></h4> <p>Please cite the support paper instead of the dataset itself. </p> <blockquote> <p>Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. <em>Journal of Open Aviation Science</em>. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a></p> </blockquote>
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
Selected data(s) from : Five-dimensional optical data storage based on ellipse orientation and fluorescence intensity in a silver-sensitized commercial glass
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p>- <strong>Figure 1.</strong> (<strong>a</strong>) Femtosecond laser tight focusing in the silver-containing glass, leading to the production of fluorescent silver clusters at its periphery. (<strong>b</strong>) SLM holographic phase masks with an additional cylindrical profile leading to an elliptical pattern by DLW. (<strong>c</strong>) Oriented elliptical patterns obtained by SLM phase mask manipulation, corresponding to 2<sup>4</sup> = 16 orientation-encoded levels. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> Fabricated fluorescence calibration matrix. (<strong>a</strong>) Confocal image of all basic storage units composed by 16 intensity levels and 16 orientation levels. (<strong>b</strong>) Measured fluorescence intensity versus incident DLW intensity for the 5D decoding process. <strong>(Pictures, opj file, csv datas)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>,<strong>b</strong>) are the encoded images of two Nobel laureates in 16 orientation levels and 16 intensity levels, respectively. (<strong>c</strong>) 100 × 100 entangled patterns among 16 × 16 intensity and orientation levels. (<strong>d</strong>) The fluorescence calibration matrix was fabricated for decoding (fluorescence excitation at 405 nm). <strong>(Pictures, cvs datas)</strong></p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_2020-10-21_V01 : Figure 3</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3a_2020-10-21_V01 : Original image oritentation</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3b_2020-10-21_V01 : Original image intensity</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3c_Figure3d_2020-10-21_V01 : DLW image</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_IICT4BF_2020-10-21_V01 : Intensity image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BF_T2020-10-21_V01 : Orientation image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BFL_2020-10-21_V01 : Orientation image converted to 4 bit format level</li> </ol> <p><strong>- Figure 4.</strong> (<strong>a</strong>,<strong>b</strong>) Retrieved images from the initial images of Figure 3a,b, respectively. (<strong>c</strong>,<strong>d</strong>) Histograms of the level difference between original and decoded levels for the orientation direction and the fluorescence intensity, respectively. (<strong>Picture and csv datas</strong>)</p> <p>- <strong>Figure 5.</strong> (<strong>a</strong>) Confocal top-view image of one single elliptically-shaped storage unit fabricated by using type A DLW. (<strong>b</strong>) Fluorescence intensity profile along the horizontal and vertical cross section at focal plane. (<strong>c</strong>) Fluorescence intensity profile and Gaussian fitting along the z-axis (depth). (<strong>Picture, opj file, csv datas</strong>)</p> <p> </p> <p> </p>
Responsive Environmental Assessment Commercially Hosted (REACH)
<p>This is the historical REACH data release. These are the “v3” files produced at The Aerospace Corporation, spanning March 2017 until December 2019. It is accompanied by a README (PDF), which includes a brief mission description, describes the features of the collection of dosimeters, discusses data availability throughout the dataset, describes data quality flags, and concludes with a data dictionary.</p>
Dataset: The impact of rose-waste compost on commercial cut rose cultivation in Kenya
<p>This dataset and these scripts supports the manuscript 'From waste to fertilizer: The impact of rose-waste compost on commercial cut rose cultivation in Kenya' as submitted to Cleaner Waste Systems. </p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we evaluated the impact of compost amendment on the yield and quality of cut roses cultivation in a large-scale commercial setting with over 7,500 rose plants. It was conducted between August 2022 and February 2024 near Lake Naivasha in Kenya. Additionally we evaluated the potential of compost to partially substitute mineral fertilizer. Yields were recorded daily and cut rose quality and physicochemical soil parameters were assessed every three to six months to comprehensively evaluate the impact of compost incorporation on cut rose production.</p>
Fishery impacts - CA commercial lobster catch and effort, and trap distribution around marine reserves
These data describe landings and fishing effort for the CA commercial spiny lobster fishery. Data are contained in two tables: 1) a time series collected by the California Fish and Wildlife (CDFW) of lobster catch (kg) and effort (lobster trap pulls) by year (1998-2020) and location (geographic fishery blocks). Catch is recorded and reported to CDFW by fish processors (buyers) with “fish tickets”. Trap pulls are recorded and reported by fishermen with logbooks. 2) The number of traps counted in the water at increasing distances from the boundary of marine reserves, three reserves located in the northern region of the fishery and three from the southern region of the fishery, in November 2022.
Real-life instances of a non-commercial indoor football league
<p>This repository accompanies the paper 'Scheduling a Non-Commercial Indoor Football League: a Tabu Search Based Approach' (Van Bulck, Goossens, Spieksma (2017)). More specifically, it stores all input instances and the generated schedules.</p>
Implications of Handover Events in commercial 5G Non-Standalone Deployments in Rome
<p>Passive and active network measurements used for analysing the implications of Handover (HO) events inn commercial 5G Non-Standalone (NSA) deployments in Rome.</p> <p> </p> <p> </p>
Dataset of imaged commercial and custom-made printing filament materials for Computed Tomography imaging of organ body phantoms
<p>The dataset includes a total of 29 filament materials 7 custom-made materials and the selection of 22 commercially available materials.</p> <p>All the materials were printed with a Longer LK4 Pro printer into cubes with dimensions 20 mm x 20 mm x 10 mm.</p> <p>A part of each filament was grinded into pellets, placed into metallic cylinder container and then were heated up to their melting points to receive a homogeneous cylindrical sample of this material.</p> <p>The cubes and the cylindrical samples were scanned at a clinical CT scanner at three anode voltages (kV) and a slice thickness of 0.6 mm.</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Home environment
<p>For the home environment we have: 01 Wifi Modem Router, 03 Smartphones, 01 server, 01 desktop, 01 Multifunction Printer, 01 network extender, 01 SmartTV, 01 Cable TV decoder and 01 firewall. This environment is a local network. The server has the Monitoring Environment and a network card, which provides connectivity and receives all network traffic for analysis.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-09-15 to 2019-02-04</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internal environment
<p>In this environment we have: 01 Wifi Router, 01 Smartphone, 01 server and 01 desktop with virtual machines. This environment, called Internal, is a local network. One of the servers has the Security and Performance Monitoring Environment installed. In addition, 05 virtual machines were instantiated via QEMU on the same network. In this server, a network card provides connectivity to the environment and the other network card receives all network traffic for analysis by the Monitoring Environment. Getting traffic to Suricata is done by Ettercap. The desktop has two virtual machines instantiated via Oracle VirtualBox, on the same network and acts on the network as a client as well.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-06-06 to 2019-01-31</p> <p> </p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment
<p>We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Environment with scalability
<p>We have used the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>We added 08 virtual machines and performed the following test with a denial of service DoS attack:</p> <p>01 virtual machine from 04:00 pm to 23:55 pm on 2019-12-04 with an interval every 01 hour;<br> 02 virtual machines from 23:55 am on 2019-12-04 to 08:50 am on 2019-12-05 with an interval every 01 hour;<br> 04 virtual machines as of 08:55 am on 2019-12-05 to 05:25 pm on 2019-12-06 with an interval every 5 minutes;<br> 08 virtual machines from 05:30 pm on 2019-12-06 to 23:59 on 2019-12-06 with an interval every 5 minutes;<br> End of tests with shutdown of virtual machines at 23:59 on 2019-12-06.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2019-12-04 to 2019-12-06</p>
SS-DBEM model results for main tuna commercial species
<p>Projected body size and potential biomass changes (in %) for the main commercial tuna species and swordfish by each RFMO by the mid- and the end-of-the-century. The changes have been estimated as the difference between the future and the reference period. A multi-species ecosystem model which integrates a species-based model (DBEM, Dynamic Bioclimatic Envelope Model) with the size-spectrum approach (SS) was used in this study. The code for the model is available <a href="https://zenodo.org/record/7548113#.Y8kwxBfMKUk">here</a>.</p>
Intermediate data for: Environment and shipping drive eDNA beta-diversity among commercial ports
<p>Intermediate data generated by Paul Czechowski as part of MEC-22-0945.R1 using code stored at <a href="https://github.com/macrobiotus/ships_and_bugs">GitHub</a>, most recently release with <a href="https://doi.org/10.5281/zenodo.7600608">DOI: 10.5281/zenodo.7600608 </a> . Pre-print with linked final manuscript version available at BioRxiv via <a href="https://doi.org/10.1101/2021.10.07.463538">DOI: 10.1101/2021.10.07.463538</a>. Please refer to the published manuscript for a full list of available digital resources associated with this work.</p>
Supplementary dataset to publication: "Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis"
<p><em>Campylobacter jejuni </em>is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of <em>Campylobacter jejuni</em> recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq<sup>®</sup> technology was used to sequence 66 <em>Campylobacter jejuni </em>isolates obtained between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. Phylogeny, resistome, plasmidome and virulome profiles were analyzed using whole-genome sequencing data. Genetic resistancemarkers were identified with bioinformatics tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance.</p>
A gonad photographs dataset for fish of commercial interest
<p>This dataset was established during a one year project under the IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer) for the harmonisation of maturity data acquisition methods for bony fish of commercial interest, with the help of scientific campaign CGFS, EVHOE, IBTS and ACCOBIOM (Auber et al., 2021, Laffargue et al.,1987, Le Roy et al., 1988).</p> <p>This dataset contains 4133 standardised gonad’s macroscopic photos of 61 species of fish of commercial interests collected along the European coastal water and the Caribbean Sea. The scale used throughout this project is the ICES maturity scale “WKASMSF” (ICES, 2018). To have more details about the photography process used for photos in this database, check the “Fish gonads’ photography protocol” from Le Meleder et al. (2022).</p> <p>This dataset is associated with a GitHub page hosting tools to generate maturity identification forms for fish of commercial interest. To have more detail about identification forms files and have the latest update, check the GitHub page “MaturityScaleTools” (<a href="https://github.com/LM-Anna/MaturityScaleTools">LM-Anna/MaturityScaleTools: Maturity scale tools to identify visual maturity phases (github.com)</a>).</p> <p>This dataset is meant to be enriched with time. Photos may be added to complete the missing maturity phases for every species of the world. To have more details about the dataset or to add new photos, please contact annalemeleder@orange.fr or <a href="mailto:laurent.dubroca@ifremer.fr">laurent.dubroca@ifremer.fr</a>.</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li> <p><strong>Photo_MATURITY.zip</strong> : archive in zip format of 4133 macroscopic photographs of gonads (.JPG; 2Mo-6Mo; sRGB; 1080p). Each photo was taken with the same camera (OLYMPUS / Tough F2.0), on the same white background, with homogeneous lighting to avoid glints from overexposure. Since there are no duplicated photos’ names because all photos were taken with the same camera, names correspond to the one generated by the camera. Photos are sorted under three levels of directories :</p> <ul> <li> <p><strong>First level :<em> species’ scientific name</em></strong> (Example : <em>Dicentrarchus labrax</em>) : there are currently 61 different species listed</p> </li> <li> <p><strong>Second level :<em> F or M</em> </strong>: the sex, with F from females and M for male</p> </li> <li> <p><strong>Third level : <em>A, B, C, D, E or F</em> </strong>: the maturity phases of the ICES 2018 scale. In each folder are assigned the corresponding gonadic photos.</p> </li> </ul> </li> </ul> <p> </p> <p><strong>Data frames:</strong></p> <ul> <li> <p><strong>photo_mat.xlsx</strong> (13 columns / 4133 rows): data table (Excel format) listing all photos in the Photo_MATURITY database, as well as the data associated with the photos. The data table is presented as followed, for each photo :</p> <ul> <li> <p>Name : Name of the photo</p> </li> <li> <p>Type : Type of gonad photo (INT = inside without organs, INT ORG = inside with organs, EXT = outside, EXT OUV = outside and open, FLUANT = fluent)</p> </li> <li> <p>sppeng : English vernacular name of the species or species group established for identification forms</p> </li> <li> <p>Species : Scientific name of the species or species group established for identification guides</p> </li> <li> <p>Sex : Sex of the fish (M = male, F = female)</p> </li> <li> <p>phase ID : visually estimated maturity phase (ICES WKASMSF scale : A, B, C, D, E or F)</p> </li> <li> <p>Link : Link to the photo, to change depending on your path to the downloaded dataset =LIEN_HYPERTEXTE(« (Your path to the dataset)\Photo_MATURITE\« &H<sub>n</sub>& »\« &E<sub>n</sub>& »\« &F<sub>n</sub>& »\« &A<sub>n</sub>& ».JPG »)*</p> </li> <li> <p>spplatTRUE : Scientific name of the species without taking species groups into account</p> </li> <li> <p>sppengTRUE : English vernacular name of the species without taking species groups into account</p> </li> <li> <p>Date : Date the photo was added to the dataset (the year correspond to the year the photo was took)</p> </li> <li> <p>Campaign : Survey during which the photo was taken</p> </li> <li> <p>Area : Geographical area (ICES or not) where the scientific survey occurred (Caribbean sea = Caribbean waters area, IVb-c = ICES area for the IBTS campaign, NA = unknown area, VIId = ICES area for NourManche campaign, VIId/VIIe = ICES area for CGFS campaign, VIIg/VIIj/VIIh/VIIIa-b = ICES area for EVHOE campaign)</p> </li> <li> <p>Commentary : Comments about the photo.</p> </li> </ul> </li> </ul> <p> </p> <p><strong>CAUTION</strong> : When using this database, please make sure to modify the link to the photos in the “Link” column with the link where you downloaded the Photo_MATURITY.zip file, and to check if it works by clicking it.</p> <p> </p> <p>*<sub>n</sub> = row number</p>
DFT Featurization of 730 Commercially Available Boronic Acids
<p>Dataset of DFT calculated features of 730 commercially available boronic acids. </p> <p>Software, scripts, input data, and workflow:</p> <p>https://github.com/Gademann-UZH/Chemical-Space-Generation</p> <p>Archived version, see</p> <p>DOI: 10.5281/zenodo.7540235</p> <p>Publication, see:</p> <p>Mechanistic Studies and Data Science-Guided Exploration of Bromotetrazine Cross-Coupling<br> Lukas V. Hoff, Gleb A. Chesnokov, Anthony Linden, and Karl Gademann<br> ACS Catalysis 2022 12 (15), 9226-9237<br> DOI: 10.1021/acscatal.2c01813 </p> <p> </p> <p>V1.1: Corrected calculations for OB(O)C1=CC=C(C=C1)C(=C(/C1=CC=CC=C1)C1=CC=C(C=C1)B(O)O)\C1=CC=CC=C1 and for OB(O)C1=CC(F)=C(OCCCN2CCOCC2)C=C1.</p>
Dataset of hair cortisol concentration in 950 finishing pigs on 20 commercial farms
<p>This dataset contains the hair cortisol concentrations of 950 finishing pigs. Pig hair was sampled as part of a study funded by the European project HealthyLivestock. Pigs were sampled in two separate batches on 20 farms (24 pigs/batch, two batches/farm. NB: 10 samples could not be analyzed at the laboratory). Farms were located in western France. A reference to the article relating to this dataset will be added when the article will be published.</p> <p>-The first sheet includes the 950 hair cortisol concentrations, distinguishing the batches and farms where pigs were sampled.</p> <p>-The second sheet includes the estimation of the average size of a pig batch on the 20 farms where hair was sampled + the estimation of the percentage of pigs sampled per batch.</p>
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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.