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47,429 results for “5”
Cp*2Yb(taphen)PtMe2, bis(pentamethyl)ytterbium(4,5,9,10,tetraazaphenanthrene)dimethylplatinum
<p>The dataset contains the raw data for the compound Cp*2Yb(taphen)PtMe2</p>
Cp*2Yb(taphen)PdMe2, bis(pentamethyl)ytterbium(4,5,9,10,tetraazaphenanthrene)dimethylpalladium
<p>The dataset contains the raw data for the compound Cp*2Yb(taphen)PdMe2</p>
WWV Doppler Shift Recording by AD8Y, 5 MHz, no GPSDO
<p>Doppler shift data from WWV on 5 MHz.</p> <p><strong>Callsign:</strong> AD8Y</p> <p><strong>Radio:</strong> Kenwood TS-450</p> <p><strong>GPSDO:</strong> No</p> <p><strong>Antenna:</strong> Random wire vertical</p> <p><strong>Coordinates:</strong> (41.4067594 -75.6675467)</p> <p><strong>Elevation:</strong> 914.8 feet</p> <p><strong>Notes:</strong> The TXCO is intermittently calibrated against a GPSDO, but the system cannot be run off the GPSDO because signal leakage from it overwhelms the signal from WWV. Data collection was interrupted on some dates, including 31 December 2019 and 16 January 2020. </p> <p> </p> <p>This dataset is part of the Frequency Analysis Network project, collected according to the same process as the data for the Festival of Frequency Measurement (10.5281/zenodo.3707210). </p>
Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study
<p>Archive of the following GitHub repository: <a href="https://github.com/romain-jacob/ST_data_and_viz">https://github.com/romain-jacob/ST_data_and_viz</a></p> <p>This repository contains the raw data and analysis scripts of a replication study of synchronous transmissions using the <a href="https://www.nordicsemi.com/en/Software%20and%20tools/Development%20Kits/nRF52840%20Dongle">nRF52840 Dongle</a> where we compare the success rate of synchronous transmission attempts using two transmitters while varying a number of parameters. The repository also contains the source code of a data visualization application, which you can either</p> <ul> <li>run locally or</li> <li><a href="https://github.com/romain-jacob/ST_data_and_viz/blob/master/URL">online in your web browser</a> at <a href="http://explore-st-data.ethz.ch/">http://explore-st-data.ethz.ch/</a></li> </ul> <p>The study is described in more details in the following publication.</p> <blockquote> <p><strong>Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study</strong><br> Romain Jacob, Anna-Brit Schaper, Andreas Biri, Reto da Forno, Lothar Thiele<br> <a href="https://cpsbench20.ethz.ch/">CPS-IoTBench'20</a><br> [ <a href="https://openreview.net/forum?id=BSZPNEUHiS2">Direct link</a> ]</p> </blockquote> <p>The dataset has been collected during the <a href="https://doi.org/10.3929/ethz-b-000375332">Master thesis of Anna-Brit Schaper.</a></p>
Supplementary material (aggregated data set): Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405
<p><strong>Meal choice at two university canteens in a field experiment during 12 weeks: aggregated menu sales data</strong></p> <p>How do canteen visitors respond to a revised offer of meat-based and plant-based meals? Selected innovations were simultaneously implemented and tested in a trans­disciplinary field experiment in two university canteens over a 12-week period in the autumn semester 2017. Throughout this time, the meat dishes and ‘veg-meals’ (ovo-lacto-vegetarian and vegan meals) were randomly distributed among the three menu lines, the veg-meals were not marketed and advertised as such and the previous vegetarian menu line was abolished. Weeks where the usual number of meat dishes were on offer (the ‘base weeks’) alternated with weeks where the share of veg-meals was increased (the ‘intervention weeks’). <br> The field experiment did not have a negative impact on the number of meals sold or the turnover compared to the two previous years. Women choose meat dishes less often than men. This connection applies in the base weeks and intervention weeks, in all age groups, among both students and among staff. Remarkably, the share of (non-labelled) vegan dishes is comparable for women and men over all age groups, independent of university affiliation (student, staff). Authentic vegan dishes were particularly welcome. Veg-meals could also be sold on the more expensive menu line. There was a better correlation between meal choice, eating habits and attitudes (health, environment, animal welfare, social aspects) than expected. <br> One quarter of canteen visitors show ‘veg-oriented’ eating habits and three quarters thereof ‘meat-oriented’ eating habits. Only a minority of potential visitors eat regularly at the canteen, and those who do exhibit meat-oriented eating habits more often. We conclude, therefore, that the canteen’s usual menu offer is primarily aimed at visitors with meat-oriented eating habits at lunchtime. The most typical visitors to the canteen are male students who select meat dishes.<br> It has been shown, therefore, that the simultaneous changes in supply have worked. Veg-meals are preferred, particularly by women and those prone to flexitarian eating habits; however, also the canteen visitors with meat-oriented eating habits chose veg-meals during the intervention weeks. Catering in canteens has the great potential to expand the range of veg-meals at the expense of meat dishes, provided that the culinary quality is of a high enough standard and meals are not offered as vegetarian or vegan. The question arises as to whether canteens are not missing an economic opportunity if they only offer traditional meat dishes? Canteens are perfectly suited as real-world laboratories in which innovations for sustainable catering can be tried out. The field experiment in the two university canteens is a start; further experiments are needed.</p> <p><strong>The data set contains more than <em>26'000</em> aggregated menu sales. The analyses and results are summarized in the working paper No. 5 <a href="https://doi.org/10.21256/zhaw-1405">https://doi.org/10.21256/zhaw-1405</a></strong></p> <p>The corresponding scripts are: </p> <p>- <a href="http://doi.org/10.5281/zenodo.4034686">10.5281/zenodo.4034686</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4034698">10.5281/zenodo.4034698</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4244258">10.5281/zenodo.4244258</a> (newer Version)</p> <p>For more information visit the <a href="http://novanimal.ch">novanimal.ch</a> website.</p>
Estimating TOF from ultrasound signal over bare steel sample of 5 mm thickness
<p>These data have been obtained using the CEIT ultrasound testbed exciting a PZT piezoelectric sensor with a +/-15 volts pulse located over a 5 mm bare steel sample. The testbed receives the ultrasound response and estimates the TOF measuring the distance between two consecutive echoes. The aim is to develop an embedded system to measure the loss of thickness produced by corrosion.</p>
Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s' Molecular Dynamics
<p><strong>Related publication:</strong><br> Soccio, M.; Martínez-Tong, D.E.; Guidotti, G.; Robles-Hernández, B.; Munari, A.; Lotti, N.; Alegria, A. Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s’ Molecular Dynamics. <em>Polymers</em> 2020, <em>12</em>, 1355.<br> <a href="https://doi.org/10.3390/polym12061355">10.3390/polym12061355</a></p> <p><strong>EUSMI proposal codes:</strong><br> E171100040, E171100043</p>
Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18'30.96" N, 11°5'0.68" E)
<p>Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18’30.96” N, 11°5’0.68” E (Latitude Y 33.3086, Longitude X 11.083522).</p> <p>1. Conventional AMS radiocarbon dates and reservoir-corrected and 2σ range calibrated ages from Sebkha Boujmel (BJM2 core).</p> <p>2. Output of the age-depth model for BJM2 core indicating depth and associated mean date for each cm (cal yr BP). The age model was obtained by third-degree polynomial regression with 10k model iteration using the package Clam 2.2.</p> <p>3. Pollen percentage for the three ecological groups (Mediterranean, steppe and desert taxa). The percentages are calculated with respect to a basic sum that only includes these three groups. Pollen taxa and types from the same genus or family and with the same ecology are grouped; including Boraginaceae (Moltkiopsis ciliata, Onosma and Echium), Ephedra sp. (Ephedra fragilis-t. and Ephedra distachia-t.) and Zygophyllaceae (Fagonia, Nitraria and Zygophyllum). Percentage of aquatics pollen are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>4. Pollen and clay mineralogy data from Sebkha Boujmel. Percentages of (1) <strong>fresh water</strong> (Cyperaceae, Glyceria, Juncus, Lemna, Potamogeton, Rumex aquaticus-t., Typha/Sparganium-t.) and <strong>(2) Mediterranean tree and shrub</strong> (Buxus, Ceratonia, Cistus, Juniperus, Lamiaceae, Myrtus, Nerium, Olea, Papaveraceae, Pinus, Pistacia, Quercus ilex-t., Quercus deciduous-t., Rhus tripartita-t.) pollen taxa. (3) <strong>Wet / dry (W / D) pollen ratio</strong> (Poaceae + Cyperaceae/Asteraceae Cichorioideae + Asteraceae Asteroideae + Amaranthaceae Cornulaca/Traganum-t.). (4) <strong>Percentages of desert pollen taxa</strong> (Apiaceae, Asphodelus, Asteraceae Asteroideae, Asteraceae Cichorioideae, Calligonum, Capparis, Cistanche, Cleome, Cornulaca/Traganum-t., Crassulaceae, Cucurbitaceae, Echium, Ephedra distachia-t., Ephedra fragilis-t., Fagonia, Helianthemum, Malvaceae, Moltkiopsis ciliata, Neurada, Nitraria, Onosma, Reaumuria, Tamarix and Zygophyllum). (5) <strong>Illite</strong> <strong>[%] (Ill) / Kaolinite [%] (Kln) ratio</strong> and (6) <strong>Palygorskite percentages [%] (Plg)</strong>.</p> <p>5. Pollen percentage of Artemisia and selected anthropogenic pollen indicators (APIs) including cultivated (Cerealia-t., Corchorus, Ficus, Olea, Phoenix, Vitis), nitrophilous (Aizoaceae, Emex, Peganum, Polygonum) and introduced (Acacia cyanophylla-t., Casuarina, Eucalyptus) plant taxa. Percentage are calculated based on the total sum of pollen grains identified in each pollen spectrum.</p> <p>6. Pollen counts for BJM2 core (pollen grain count for each taxon by sample). + Lycopodium (added), Lycopodium (counted) and Sample weight [gr].</p> <p>7. Clay Mineralogy of BJM2 sediment core. </p> <p>Smectite [%] (Sme), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Illite [%] (Ill), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Palygorskite [%] (Plg), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Kaolinite [%] (Kln), METHOD/DEVICE: X-ray diffraction, clay fraction</p> <p>Chlorite [%] (Chl), METHOD/DEVICE: X-ray diffraction, clay fraction</p>
IPBES Data Management Tutorials - Chapter 5: Tools for data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management Policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p> <p>This chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle. The chapter includes the following sessions: </p> <ul> <li>Session 5-1: Introduction to tools for data management (<a href="https://doi.org/10.5281/zenodo.4018639">10.5281/zenodo.4018639</a>)</li> <li>Session 5-2: Tools to find and attribute DOIs (<a href="https://doi.org/10.5281/zenodo.4018643">10.5281/zenodo.4018643</a>)</li> <li>Session 5-3: Literature access tools (<a href="https://doi.org/10.5281/zenodo.4014824">10.5281/zenodo.4014824</a>)</li> <li>Session 5-4: Processing and analysis (<a href="https://doi.org/10.5281/zenodo.4018647">10.5281/zenodo.4018647</a>)</li> <li>Session 5-5: References and citation manager: Zotero (<a href="https://doi.org/10.5281/zenodo.4018653">10.5281/zenodo.4018653</a>)</li> <li>Session 5-6: Publishing and sharing (<a href="https://doi.org/10.5281/zenodo.4018655">10.5281/zenodo.4018655</a>)</li> </ul>
SH2-containing-inositol-5-phosphatases (INPP5D); A Target Enabling Package
<p>SH2-containing-inositol-5-phosphatases (SHIP1 and SHIP2, coded for by genes <em>INPP5D</em> and <em>INPPL1</em>, respectively) dephosphorylate phosphatidylinositol-3,4,5-trisphosphate (PI(3,4,5)P<sub>3</sub>) to produce phosphatidylinositol-3,4-bisphosphate (PI(3,4)P<sub>2</sub>). This is an important part of the PI3K/AKT/mTOR signalling pathway. The SHIPs have been linked to a range of conditions, including cancer, diabetes, hypertension, and graft versus host disease. A link has also been demonstrated by GWAS between a non-coding mutation in the <em>INPP5D</em> (SHIP1) gene and increased risk of late-onset Alzheimer’s disease. The role of SHIP1 in Alzheimer’s disease is believed to be mediated through inflammatory processes such as the regulation of microglia and cytokine release. The distinction between the effects of SHIP1 and SHIP2 on these processes is not fully understood. In this TEP we present an apo structure of the phosphatase and C2 domains of SHIP1 and a structure with a magnesium ion and a phosphate ion bound to the active site. We also present 91 fragment bound structures that may act as starting points for the modulation of SHIP1. We are also able to crystallise an equivalent construct of SHIP2 and purify a range of other inositol-5-phosphatases to serve as a selectivity panel for the development of specific compounds. An assay has also been developed.</p>
Agonum rugicolle, Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.
<p>New version as *png</p> <p>Agonum rugicolle,</p> <p>Fig_5 from Assmann et al. (2021) The ground beetle tribe Platynini Bonelli, 1810 (Coleoptera, Carabidae) in the southern Levant: dichotomous and interactive identification tools, ecological traits, and distribution.</p>
Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available at a spatial resolution of 1° and 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° or 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0)
We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available on a spatial resolution of 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.
PubChemLite for Exposomics + predicted CCS from CCSbase - 5 Sept. 2025
<p>PubChemLite is a subset of PubChem (<a href="https://pubchem.ncbi.nlm.nih.gov/">https://pubchem.ncbi.nlm.nih.gov/</a>) selected from major categories of the Table of Contents page at the PubChem Classification Browser (<a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72">https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72</a>). This version of PubChemLite for Exposomics has predicted collision cross section (CCS) values for 11 adducts provided by Libin Xu and team at CCSbase (<a href="https://ccsbase.net/">https://ccsbase.net/</a>) calculated from the latest <a href="https://github.com/dylanhross/c3sdb/">c3sdb</a> code.</p> <p>PubChemLite <em>exposomics</em> is compiled from 11 categories: AgroChemInfo, BioPathway, DrugMedicInfo, FoodRelated, PharmacoInfo, SafetyInfo, ToxicityInfo, KnownUse, DisorderDisease, Identification, ChemClass.</p> <p>CCS adducts provided are: [M+H]+, [M-H]-, [M+Na]+, [M+K]+, [M+NH4]+, [M+H-H2O]+, [M+HCOO]-, [M+CH3COO]-, [M+Na-2H]-, [M]+, [M]-</p> <p>Details on the CCS prediction are given here: Ross <em>et al</em>. (2020) Analytical Chemistry, DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.9b05772">10.1021/acs.analchem.9b05772</a></p> <p>PubChemLite is described in Schymanski <em>et al. </em>(2021) J. Cheminformatics, DOI: <a href="https://doi.org/10.1186/s13321-021-00489-0" target="_blank" rel="noopener">10.1186/s13321-021-00489-0</a></p> <p>An article describing these joint efforts is available: Elapavalore <em>et al</em>. (2025) ES&T Letters, DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p>PubChemCIDs have been collapsed by InChIKey first block, reporting the structure from the most annotated CID, plus related CIDs. Entries that will be ignored by MetFrag (salts, disconnected substances) or cause errors (e.g. transition metals) have been removed. The Patent and PubMed ID counts are extracted from files on the PubChem FTP site. The "AnnoTypeCount" term counts how many of the categories are represented, the subsequent column (named per category) counts the number of annotation categories available in the next sub-category of the TOC entry.</p> <p>These files can be used "as is" as localCSV for MetFrag Command Line (<a href="https://ipb-halle.github.io/MetFrag/">https://ipb-halle.github.io/MetFrag/</a>) - please do NOT upload these files directly to the web interface, they are too large and will be available in a drop-down menu.</p> <p>Further details are described in Schymanski <em>et al.</em> (2021) DOI:<a href="https://doi.org/10.1186/s13321-021-00489-0">10.1186/s13321-021-00489-0</a> and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a></p> <p><strong><em>NOTE: The latest PubChemLite for Exposomics version can be downloaded at DOI:</em></strong><em><strong><a href="https://doi.org/10.5281/zenodo.5995885">10.5281/zenodo.5995885</a> (currently updating monthly). This file will be updated shortly after. <br></strong></em></p> <p>Please cite this data source and Elapavalore <em>et al</em>. (2025) DOI: <a href="https://doi.org/10.1021/acs.estlett.4c01003">10.1021/acs.estlett.4c01003</a> when using this dataset.</p>
Spectrophotometric Assay for the Detection of 2,5-Diformylfuran and Its Validation through Laccase-Mediated Oxidation of 5-Hydroxymethylfurfural
<p>Modern biocatalysis requires fast, sensitive, and efficient high-throughput screening methods to screen enzyme libraries in order to seek out novel biocatalysts or enhanced variants for the production of chemicals. For instance, the synthesis of bio-based furan compounds like 2,5-diformylfuran (DFF) from 5-hydroxymethylfurfural (HMF) via aerobic oxidation is a crucial process in industrial chemistry. Laccases, known for their mild operating conditions, independence from cofactors, and versatility with various substrates, thanks to the use of chemical mediators, are appealing candidates for catalyzing HMF oxidation. Herein, Schiff-based polymers based on the coupling of DFF and 1,4-phenylenediamine (PPD) have been used in the set-up of a novel colorimetric assay for detecting the presence of DFF in different reaction mixtures. This method may be employed for the fast screening of enzymes (Z' values ranging from 0.68 to 0.72). The sensitivity of the method has been proved, and detection (8.4 μM) and quantification (25.5 μM) limits have been calculated. Notably, the assay displayed selectivity for DFF and enabled the measurement of kinetics in DFF production from HMF using three distinct laccase–mediator systems.</p>
Subset Data 5: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 10801 to 13500)
<p><em><strong>Subset Data 5: 2700 Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes (Models 10801 to 13500)</strong></em></p> <ul> <li>2700 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB.</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
TN412 SCOPE-Gradients-5 cruise, Wirewalker
<p>This dataset was collected during the 5th SCOPE-Gradients cruise. The SCOPE-Gradients program was designed to test conceptual and mathematical models of biogeochemical organization across the transition zone between different ecosystems. The Wirewalker is a wave-powered drifting profiler designed to collect autonomous, vertically-resolved oceanographic observations. The profiler moves along a wire that is suspended between a float at the sea surface and a weight placed at a depth of tens or hundreds of meters. The system horizontal displacement follows the ocean currents. Wirewalker measurements include temperature, salinity, depth, beam attenuation, backscattering, chlorophyll fluorescence, CDOM fluorescence, downwelling irradiance in four spectral bands, and dissolved oxygen concentration.</p>
Growth of wild cucumber (Echinocystis lobata) in three variants of support (5, 20, 50 cm) and shoot geometry
<p><span>The experiments included three variants of support: 5 cm, 20 cm, and 50 cm. </span><span>The data was collected in the years 2020-2023. After developing their first pair of mature leaves, when the plants were about 20-35 cm in length, the plants were photographed. The shutter was released every 15 minutes. The resulting images were combined into a 10 fps video. The following were used for the analysis: 13 recordings of the 5 cm variant and 12 recordings of the 20 and 50 cm variants.</span></p> <p><span>Next, an analysis was performed based on the resulting video using Tracker</span><span> for kinetic analysis of video objects. The recordings were used to measure the plant growth parameters. The tape measure and point mass tools were used to determine the length of the shoots and to change the position of the apex relative to the areas of the X- and Y-axis photographed over time. To compensate for this, a trend line was drawn (polynomial of the 2nd degree, due to the very good fit, R<sup>2</sup> = 0,975–0,995) and new plant length parameters were calculated. </span></p> <p><span>To enhance the research, cross-sections of the shoots of ten plants were scanned. Samples were taken every 5 cm from the base of the plant. Using ImageJ</span><span> the following parameters were measured: shoot cross-sectional area, tissue area, perimeter, and circularity. </span></p>
Series production data set for 5-axis CNC milling
<p>The data set described encompasses features extracted from the machine control of a five-axis milling machine across thirteen series of productions. Each production series entails a setup changeover to prepare the machine for another product type. Alongside timestamps and twenty features derived from Numerical Control (NC) variables, the data set includes labels denoting various production phases. These labels, up to 23 in total, are structured around a generalized milling process. Comprising thirteen .csv files, each corresponding to a series production, the dataset was gathered within a production company operating in the contract manufacturing sector. These components are tied to actual series orders within ongoing industrial production.</p> <p>The complete description of the data set is published here: <a href="https://www.mdpi.com/2306-5729/9/5/66" target="_blank" rel="noopener">https://doi.org/10.3390/data9050066</a></p>
DCASE 2024 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2024 "Few-shot Bioacoustic Event Detection" consists of 217 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB24/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___RD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_set_annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br>TRAINING SET<br>-----------------------------------------------------<br>Number of audio recordings | 174<br>Total duration | 21 hours<br>Total classes | 47<br>Total events | 14229<br>-----------------------------------------------------<br>TRAINING SET/BV<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 10 hours<br>Total classes | 11<br>Total events | 9026<br>Sampling rate | 24000 Hz<br>-----------------------------------------------------<br>TRAINING SET/HT<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 5 hours<br>Total classes | 5<br>Total events | 611<br>Sampling rate | 6000 Hz<br>-----------------------------------------------------<br>TRAINING SET/JD<br>-----------------------------------------------------<br>Number of audio recordings | 1<br>Total duration | 10 mins<br>Total classes | 1<br>Total events | 357<br>Sampling rate | 22050 Hz<br>-----------------------------------------------------<br>TRAINING SET/MT<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 1 hour and 10 mins<br>Total classes | 4<br>Total events | 1294<br>Sampling rate | 8000 Hz<br>-----------------------------------------------------<br>TRAINING SET/WMW<br>-----------------------------------------------------<br>Number of audio recordings | 161<br>Total duration | 4 hours and 40 mins<br>Total classes | 26<br>Total events | 2941<br>Sampling rate | various sampling rates<br>-----------------------------------------------------</p> <p>-----------------------------------------------------<br>VALIDATION SET<br>-----------------------------------------------------<br>Number of audio recordings | 43<br>Total duration | 49 hours and 57 minutes<br>Total classes | 7<br>Total events | 3504<br>-----------------------------------------------------<br>VALIDATION SET/HB<br>-----------------------------------------------------<br>Number of audio recordings | 10<br>Total duration | 2 hours and 38 minutes<br>Total classes | 1<br>Total events | 712<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 3 hours<br>Total classes | 2<br>Total events | 292<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/ME<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 20 minutes<br>Total classes | 2<br>Total events | 73<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB24<br>-----------------------------------------------------<br>Number of audio recordings | 4<br>Total duration | 2 hours<br>Total classes | 2<br>Total events | 350<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/RD<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 18 hours<br>Total classes | 1<br>Total events | 1372<br>Sampling rate | 48000 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PW<br>-----------------------------------------------------<br>Number of audio recordings | 15<br>Total duration | 24 hours<br>Total classes | 1<br>Total events | 705<br>Sampling rate | 96000 Hz<br>-----------------------------------------------------</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br>---------------------<br>Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br>---------------------<br>Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2024_task5_training_set_classes.csv and DCASE2024_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. Additionally, DCASE2024_task5_validation_set_classes.csv also provides a recording names column.</p> <p>DCASE2024_task5_training_set_classes.csv<br>---------------------<br>dataset, class_code, class_name</p> <p>DCASE2024_task5_validation_set_classes.csv<br>---------------------<br>dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1 June 2024</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Burooj Ghani - burooj.ghani@naturalis.nl | Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p> <p>Alternately, join us on Slack: <a href="https://join.slack.com/t/dcase/shared_invite/zt-12zfa5kw0-dD41gVaPU3EZTCAw1mHTCA">task-fewshot-bio-sed</a></p> <p> </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.