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Fig. 82. Prionocyclus wyomingensis Meek, 1876. USNM 7729D. This specimen bears the label 7729 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 82. Prionocyclus wyomingensis Meek, 1876. USNM 7729D. This specimen bears the label 7729 in the same hand as the figured syntypes, and appears to be a previously unrecognized syntype, now paralectotype of the species. Middle Turonian Scaphites warreni Zone, Wall Creek Member of the Frontier Formation near Medicine Bow, Carbon County, Wyoming. Figure is X0.9.
Text-fig. 1. Location of fossiliferous localities in the Ashawq Formation in south-western Oman (the star labelled Thaytiniti shows the area depicted in Text-fig. 2). in Large Mammals From The Rupelian Of Oman - Recent Finds
Text-fig. 1. Location of fossiliferous localities in the Ashawq Formation in south-western Oman (the star labelled Thaytiniti shows the area depicted in Text-fig. 2).
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct.
Text-fig. 5. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in tympanic view; the posttympanic process of the squamosal and some possible entotympanic are also included. a – isosurface; b – line drawing with labels. Numbers 1 to 4 indicate depressions on medial flange. Abbreviations: ams – anteromedial septum, aptt – anteroventral process of tegmen tympani, cct – canal for chorda tympani nerve, ci – crista interfenestralis, cof – cochlear fossula, cp – crista parotica, ctp – caudal tympanic process, en? – possible entotympanic, epc – epitympanic crest, ew – epitympanic wing, fc – facial canal, fv – fenestra in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 5. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in tympanic view; the posttympanic process of the squamosal and some possible entotympanic are also included. a – isosurface; b – line drawing with labels. Numbers 1 to 4 indicate depressions on medial flange. Abbreviations: ams – anteromedial septum, aptt – anteroventral process of tegmen tympani, cct – canal for chorda tympani nerve, ci – crista interfenestralis, cof – cochlear fossula, cp – crista parotica, ctp – caudal tympanic process, en? – possible entotympanic, epc – epitympanic crest, ew – epitympanic wing, fc – facial canal, fv – fenestra
Figure 156-161. Lectotypes and labels. 156-158 in Pleasing fungus beetles of the West Indies (Coleoptera: Erotylidae: Erotylinae)
Figure 156-161. Lectotypes and labels. 156-158) Lectotype Ischyrus tarsalis Lacordaire, dorsal habitus with label, ventral habitus and head and pronotum. 159-161) Lectotype Oocyanus tarsatus Lacordaire dorsal habitus, ventral habitus and labels.
Fig. 2. Labels. a. Oplognathus bahianus Ohaus, 1912 in Taxonomic revision of the genus Oplognathus MacLeay, 1819 (Coleoptera: Scarabaeidae: Rutelinae: Rutelini)
Fig. 2. Labels. a. Oplognathus bahianus Ohaus, 1912, syntype, ♂. b. O. helmenreichi Ohaus, 1905, holotype, ♂. c–d. O. helmenreichi var. maculicollis Ohaus, 1914. c. Syntype ♂. d. Syntype ♀. e. O. kirbii MacLeay, 1819, neotype, ♂.
Fig. 5. Namkongnaia lemeslei gen. et comb. nov. A. Labels associated with the syntype lot. B in Molecular phylogeny reveals a new genus of freshwater mussels from the Mekong River Basin (Bivalvia: Unionidae)
Fig. 5. Namkongnaia lemeslei gen. et comb. nov. A. Labels associated with the syntype lot. B. Original figure (after Morelet 1875: pl. 14 fig. 1). C–D. Syntype MNHN MP 3150 (photographs by V. Heros and M. Caballer) from Battambang Province, Cambodia. E. Specimen MUMNH-UNI2669. F. Specimen MUMNH-UNI2829 from Kampong Kdei River, Siem Reap Province, Cambodia. Scale bars: 10 mm.
OCTA image dataset with label annotation for quality assessment
<p>This dataset is publish by the research "<em>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</em>"</p> <p>Detail:</p> <p>OCTA image dataset with label annotation for quality assessment. sOCTA-3x3-10k: 10,480 3 × 3 mm<sup>2</sup> superficial vascular layer OCTA (sOCTA) images divided into three classes; sOCTA-6x6-14k: 14,042 6 × 6 mm<sup>2</sup> sOCTA images divided into three classes. </p> <p>GitHub: <a href="https://github.com/shanzha09/COIPS">https://github.com/shanzha09/COIPS</a></p> <p>These datasets are public available, if you use the dataset or our system in your research, please <strong>cite</strong> our paper: <em><code>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</code></em>.</p> <p>arXiv:<a href="https://arxiv.org/abs/2107.10476v1">https://arxiv.org/abs/2107.10476v1</a></p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
GeneWiki subset created with WDSub at Biohackathon 2022 - Turtle format - Only labels en english
<p>Subset of Wikidata obtained using this Shape Expression: https://github.com/kg-subsetting/datasets-biohackathon2022/blob/main/GeneWiki/GeneWiki.shex</p> <p>And the wdsub tool version 0.0.31: https://github.com/weso/wdsub which is available at docker</p> <p>The input dump is: wikidata-20220630-all.json.gz</p> <p>And the dumpformat is Turtle/RDF</p>
Hourly detections of echolocation clicks in Hawaiian Island HARP data from Hawai`i, Kaua`i, and Manawai with species labels
<p>This dataset consists of counts of detections of echolocation clicks at three sites in the Hawaiian Islands Archipelago. These sites are Hawaii, Kauai, and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into one-minute bins and each bin was given a species' label. The data provided here is further binned at an hourly level, where counts of a given species represent the number of one-minute bins within a given hour that were labeled as that species (up to a maximum of 60). One file is provided per site, and files are in .csv format that can be read using any desired coding language.<span> </span></p>
SDG Knowledge Hub Dataset of SDG-labeled News Articles
<p>Dataset of articles published on the IISD SDG Knowledge Hub (<a href="http://sdg.iisd.org/">sdg.iisd.org</a>). The SDG Knowledge Hub is an online resource publishing news and commentary regarding the implementation of the United Nations’ 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs). Labels assigned by the authors and validated by SDG Knowledge Hub editors indicate which of the 17 SDGs an article addresses.</p> <p>The data set was generated for the following publications. Please consider citing the publication if you use the data. </p> <p>Wulff, D. U., Meier, D. S., & Mata, R. (2023). Using novel data and ensemble models to improve automated labeling of Sustainable Development Goals. <em>arXiv preprint arXiv:2301.11353</em>.</p> <p>The data contain 9,172 articles downloaded on September 15th, 2021, and are shared with permission from the SDG Knowledge Hub.</p> <p>The comma-separated data file includes the following columns:</p> <p>url - URL of the article.</p> <p>title - Title of the article.</p> <p>type - Type of article. Either "News", "Policy briefs", "Guest articles", or "Generation 30". </p> <p>text - Text of the article. </p> <p>date - Publishing date of article.</p> <p>sdgs - SDG labels assigned by authors and editors. </p> <p>SDG-01 to SDG-17 - SDG indicators extracted from the author and editor label. </p>
Recombinant Expression and Chemical Amidation of Isotopically Labeled Native Melittin
<p><strong>Table S1. </strong>Amidated cationic membrane-lytic and antimicrobial peptides. There are myriad cationic membrane-lytic and antimicrobial peptides consisting of a C-terminal amidation post-translational modification that are of high interest to researchers and which our recombinant expression followed by chemical amidation could be immediately applied to. Below is a short representative list of such peptides.</p> <p><strong>Table S2</strong>. Melittin backbone chemical shifts (ppm). Deuterium isotope shift corrections were applied to all chemical shifts and pressure corrections were applied to the pressure denatured monomer chemical shifts. All shifts were measured on a 1.0-mM <sup>2</sup>H,<sup>13</sup>C,<sup>15</sup>N-labeled melittin sample in 25-mM potassium phosphate buffer, pH 7.0, 50-mM NaCl, 3% D2O. The folded tetramer peaks were measured at atmospheric pressure, while the pressure denatured monomer resonances were measured at 2.25 kbar. </p> <p><strong>Table S3.</strong> Melittin experimental <sup>15</sup>N-<sup>1</sup>H isotropic J-couplings, (J+<em><sup>1</sup>D<sub>NH</sub></em>) anisotropic couplings and <em><sup>1</sup>D<sub>NH</sub></em> residual dipolar couplings. All couplings and errors are reported in Hz. Isotropic J-couplings were not measured for G3 and A4. In these cases, an average J-coupling of -93.3 Hz was used with an uncertainty of 1.1 Hz..</p> <p><strong>Table S4</strong>. Analysis of the AlphaFold-Multimer structural models with respect to the melittin RDCs measured with the Pf1 alignment media. SVD fitting of the RDC alignment tensor parameters was done for residues 3-23 in the AF-M models and residues 3-25 in the 2MLT crystal structure. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p><strong>Table S5</strong>. Analysis of the AlphaFold-Multimer structural models with respect to the melittin RDCs measured with a stretched polyacrylamide gel alignment media. SVD fitting of the RDC alignment tensor parameters was done for residues 4-23 in the AF-M models and residues 4-25 in the 2MLT crystal structure. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p><strong>Table S6.</strong> RDC based ranking of melittin AlphaFold-Multimer structural models and the 2MLT crystal structure. Models are ranked according to the average Q factor in the two alignment media used in this study: Pf1 and a positively charged stretched polyacrylamide gel. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p> <strong>Table S7</strong>. 2MLT crystal structure and AlphaFold-Multimer atomic coordinates. The X-ray crystal structure was retrieved from the protein data bank (PDB), then it was symmetry expanded into a tetramer, residues were renumbered as described in <em>3.3.3 Comparison of <sup>1</sup>D<sub>NH</sub> couplings with the crystal structure and AlphaFold-Multimer model,</em> and finally protons were added to the structure with DYNAMO. The C-terminus column lists the C-terminal modifications for the AF-M structures in comparison to melittin’s native -CONH<sub>2</sub> C-terminus. </p> <p>Atomic coordinates in the form of .pdb files for all RDC SVD fitting done in this study:</p> <table> <tbody> <tr> <td>2mlt.pdb</td> </tr> <tr> <td>af1.pdb</td> </tr> <tr> <td>af2.pdb</td> </tr> <tr> <td>af3.pdb</td> </tr> <tr> <td>af4.pdb</td> </tr> <tr> <td>af5.pdb</td> </tr> <tr> <td>afd1.pdb</td> </tr> <tr> <td>afd2.pdb</td> </tr> <tr> <td>afd3.pdb</td> </tr> <tr> <td>afd4.pdb</td> </tr> <tr> <td>afd5.pdb</td> </tr> <tr> <td>afg1.pdb</td> </tr> <tr> <td>afg2.pdb</td> </tr> <tr> <td>afg3.pdb</td> </tr> <tr> <td>afg4.pdb</td> </tr> <tr> <td>afg5.pdb</td> </tr> <tr> <td>afk1.pdb</td> </tr> <tr> <td>afk2.pdb</td> </tr> <tr> <td>afk3.pdb</td> </tr> <tr> <td>afk4.pdb</td> </tr> <tr> <td>afk5.pdb</td> </tr> <tr> <td>afr1.pdb</td> </tr> <tr> <td>afr2.pdb</td> </tr> <tr> <td>afr3.pdb</td> </tr> <tr> <td>afr4.pdb</td> </tr> <tr> <td>afr5.pdb</td> </tr> </tbody> </table> <p> </p>
Topic Modeling for Multi-label Research Articles
<p>The abstract and title for a set of research articles, and their assigned topics. The research article abstracts and titles are sourced from the following 6 topics:</p> <ol> <li> <p>Computer Science</p> </li> <li> <p>Physics</p> </li> <li> <p>Mathematics</p> </li> <li> <p>Statistics</p> </li> <li> <p>Quantitative Biology</p> </li> <li> <p>Quantitative Finance</p> </li> </ol> <p>Note that a research article can possibly have more than 1 topic. For more details check out the paper of :</p> <p>"Evaluation of SVM Transformations for Multi-Label Research Article Classification"</p>
Metalloprotein AlphaFold set with enzyme/non-enzyme labeled sites
<pre>The AlphaFold set contains computationally generated structures for metalloproteins that were used to test MAHOMES II's enzyme/non-enzyme predictive performance (Feehan et al. 2023). README.md - Detailed description of AlphaFold set generation. AF-...-model_v2.pdb - Files with the 3D atomic coordinates of a metalloprotein. MAHOMES-II_AlphaFold_set_site_data.csv - Contains the data used during the generation of the AlphaFold set for the final sites. Columns are - Entry: The UniProt accession number of the protein with the bound metal site. - struc_id: The structures AlphaFold DB name (Febuary 2022) and the name of the file in this directory with added metal site. - metal_resName: The two letter PDB residue abbreviation for the site's metal - metal_seqID: The residue index number for the added metal ion. - Enzyme: The enzyme (True) or non-enzyme (False) label. - Entry name: UniProt entry name. - Protein names: The UniProt provided metalloprotein name(s). - Number of homologs with solved structures (PDB): Number of protein sequences in the PDB (May 21, 2020) with an E-value < 1. - Number of homologs in MAHOMES II dataset and T-metal-sites10: Number of protein sequences used to train and evaluate MAHOMES II with an E-value < 1 (0 for all entries). - Metal binding note: UniProt metal binding note that includes information covering the metal’s identity and catalytic flag. - Metal coordinating residue seqIDs: The sequence indices for the metal coordinating residues included in the UniProt’s metal binding section.</pre>
NLBSE22 urls of issues with single label
<p>NLBSE22 urls, removed the ones with multiple label on github / removed also the one which were deleted from github (unable to retrieve the labels)</p>
Global clinopyroxene phenocryst with label of hydrogen diffusion
<p>Each row of the "clinopyroxene phenocryst" dataset represents one sample. The dataset includes the content of major elements (apfu), the content of water (ppm), and the label of whether hydrogen diffusion has occurred in the sample. The dataset also includes reference and geological background information.</p> <p> </p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
The impact of pharmaceutical form and simulated side effects in an open-label-placebo RCT for improving psychological distress in highly stressed students (Open Data and Open Materials)
<p> Open-label placebo (OLP) may be utilized to reduce psychological distress. Yet, potential contextual effects have not been explored. We investigated the impact of pharmaceutical form and the simulation of side effects in a parallel group RCT (DRKS00030987). A sample of 177 highly stressed university students at risk of depression were randomly assigned by computer generated tables to a one-week intervention with active or passive OLP nasal spray or passive OLP capsule or a no-treatment control group. After the intervention, groups differed significantly in depressive symptoms but not regarding other outcomes of psychological distress (stress, anxiety, sleep quality, somatization), well-being or treatment expectation. OLP groups benefitted significantly more compared to the no-treatment control group (<em>d</em>=.40), OLP nasal spray groups significantly more than the OLP capsule group (<em>d</em>=.40) and the active OLP group significantly more than the passive OLP groups (<em>d</em>=.42). Interestingly, before intervention, most participants, regardless of group assignment, believed that the OLP capsule would be most beneficial. The effectiveness of OLP treatments seems to be highly influenced by the symptom focus conveyed by the OLP rationale. Moreover, pharmaceutical form and simulation of side effects may modulate efficacy, while explicit treatment expectation seems to play a minor role.</p>
Crowds & Machines Next level: Meditteranean wheat classification labels from gamified crowd-sourcing
<p>Machine learning (and especially deep learning) algorithms need lots of training and validation datasets, which are often unavailable. Creating on-ground datasets is costly and time consuming. Within the European Space Agency funded project ‘Crowds & Machine – Next Level’ (by <a href="https://www.blackshore.eu">Blackshore B.V.</a>, <a href="https://www.52impact.nl">52impact B.V.</a> and <a href="https://hcss.nl">The Hague Centre for Strategic Studies</a>), we aimed to solve this issue by generating labelled data effectively using an innovative gamified crowdsourced-based method.</p> <p>The objective of the project ‘Crowds & Machines Next Level’ was to generate labelled data for the training and validation of machine learning algorithms to classify the crop wheat. We make those labelled datasets freely available as open data to organisations that use machine learning for their activities, mainly companies and knowledge institutes. As part of the project we developed example scripts (Jupyter notebooks) that enable organisations to use the crowdsourced generated data smoothly for their own machine learning systems. </p> <p>BlackShore has developed the online platform Cerberus to enable large scale generation of labelled datasets, which is deployed on twenty locations around the Mediterranean Sea to generate labelled datasets of wheat and other land cover classes (see table). Those different locations encompass a diversity of climate regions, harvest cultures and crop calendars, posing a challenge to the training of machine learning algorithms. Gamers click on hexagons plotted on top of very high resolution satellite imagery (captured during the harvest period in 2021), and by combining 3 different hexagon grids those clicks are converted into triangles. Each triangle has a number of clicks (by different users) per land cover category, which provides a measure of accuracy to the label.</p> <p>52impact developed example tutorials to use the data to train pixel-based (Random Forest) and segmentation-based (U-Net) machine learning models, using Sentinel-2 imagery (provided in the data folder), which can be forked here: <a href="https://bitbucket.org/52impact/crowds-machines">https://bitbucket.org/52impact/crowds-machines</a>.<br> </p> <table> <caption><strong>Overview of locations</strong></caption> <thead> <tr> <th scope="col">ID</th> <th scope="col">location_id</th> <th scope="col">Country</th> <th scope="col">Region</th> <th scope="col">Shape</th> <th scope="col">Harvest period</th> <th scope="col">VHR image date</th> <th scope="col">S-2 pre-harvest</th> <th scope="col">S-2 harvest</th> <th scope="col">S-2 post-harvest</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>portugalAlentejo</td> <td>Portugal</td> <td>Alentejo</td> <td>01_Portugal_Alentejo_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>07/07/2021</td> <td>14/05/2021</td> <td>13/07/2021</td> <td>22/08/2022</td> </tr> <tr> <td>02</td> <td>spainAndalusia</td> <td>Spain</td> <td>Andalusia</td> <td>02_Spain_Andalusia_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>02/07/2021</td> <td>16/05/2021</td> <td>15/07/2021</td> <td>03/09/2021</td> </tr> <tr> <td>03</td> <td>spainAragon</td> <td>Spain</td> <td>Aragon</td> <td>03_Spain_Aragon_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>26/10/2021</td> <td>20/05/2021</td> <td>19/07/2021</td> <td>05/09/2021</td> </tr> <tr> <td>04</td> <td>franceAude</td> <td>France</td> <td>Aude</td> <td>04_France_Aude_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>22/09/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>05</td> <td>franceCamargue</td> <td>France</td> <td>Camargue</td> <td>05_France_Camargue_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>07/10/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>06</td> <td>franceProvence</td> <td>France</td> <td>Provence</td> <td>06_France_Provence_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>26/10/2021</td> <td>19/05/2021</td> <td>17/08/2021</td> <td>20/11/2021</td> </tr> <tr> <td>07_08</td> <td>italyMarche</td> <td>Italy</td> <td>Marche (East and West)</td> <td>07_08_Italy_Marche_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>09/08/2021</td> <td>26/05/2021</td> <td>25/07/2021</td> <td>20/11/2021</td> </tr> <tr> <td>09</td> <td>italySardinia</td> <td>Italy</td> <td>Sardinia</td> <td>09_Italy_Sardinia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>31/08/2021</td> <td>26/05/2021</td> <td>22/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>10</td> <td>italySicily</td> <td>Italy</td> <td>Sicily</td> <td>10_Italy_Sicily_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/09/2021</td> <td>22/05/2021</td> <td>26/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>11</td> <td>italyPugliaNorth</td> <td>Italy</td> <td>Puglia (North)</td> <td>11_Italy_PugliaNorth_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>06/10/2021</td> <td>11/06/2021</td> <td>31/07/2021</td> <td>04/10/2021</td> </tr> <tr> <td>12</td> <td>italyPuglia</td> <td>Italy</td> <td>Puglia</td> <td>12_Italy_Puglia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/08/2021</td> <td>03/06/2021</td> <td>02/08/2021</td> <td>21/10/2021</td> </tr> <tr> <td>13</td> <td>greeceWest</td> <td>Greece</td> <td>West</td> <td>13_Greece_West_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>02/09/2021</td> <td>27/07/2021</td> <td>05/10/2021</td> <td>14/12/2021</td> </tr> <tr> <td>14</td> <td>greeceThessaly</td> <td>Greece</td> <td>Thessaly</td> <td>14_Greece_Thessaly_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>14/07/2021</td> <td>27/07/2021</td> <td>25/09/2021</td> <td>19/12/2021</td> </tr> <tr> <td>15</td> <td>greeceMacedoniaCentral</td> <td>Greece</td> <td>Macedonia (Central)</td> <td>15_Greece_MacedoniaCentral_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>22/07/2021</td> <td>13/05/2021</td> <td>22/07/2021</td> <td>15/09/2021</td> </tr> <tr> <td>16</td> <td>greeceMacedoniaEast</td> <td>Greece</td> <td>Macedonia (East)</td> <td>16_Greece_MacedoniaEast_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>05/08/2021</td> <td>25/05/2021</td> <td>29/07/2021</td> <td>27/10/2021</td> </tr> <tr> <td>17</td> <td>greeceRhodes</td> <td>Greece</td> <td>Rhodes</td> <td>17_Greece_Rhodes_SELECTION</td> <td>15 May - 1 Jul</td> <td>09/05/2021</td> <td>25/03/2021</td> <td>24/05/2021</td> <td>22/08/2021</td> </tr> <tr> <td>18</td> <td>cyprusLarnaca</td> <td>Cyprus</td> <td>Larnaca</td> <td>18_Cyprus_Larnaca_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>19/03/2021</td> <td>07/06/2021</td> <td>21/08/2021</td> </tr> <tr> <td>19</td> <td>turkeyCyprus</td> <td>Cyprus (T)</td> <td>Farmagusta</td> <td>19_Turkey_Cyprus_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>29/03/2021</td> <td>17/06/2021</td> <td>26/08/2021</td> </tr> <tr> <td>20</td> <td>egyptBehera</td> <td>Egypt</td> <td>Behera</td> <td>20_Egypt_Behera_SELECTION</td> <td>1 Apr - 1 Jul</td> <td>06/03/2021</td> <td>26/01/2021</td> <td>07/03/2021</td> <td>19/08/2021</td> </tr> </tbody> </table> <p>The following data is provided:</p> <ul> <li>Triangulated_data.zip: contains per region and per category a geopackage (gpkg) file containing triangular polygons with the number of clicks per polygon. The filename of the polygon files depends on the location and category. For example, a file that contains the triangles corresponding to Cattle in Alentejo, Portugal, is called: 01_Portugal_Alentejo_Cattle.gpkg</li> <li>Data.zip: all data necessary to run the Jupyter notebooks, i.e., location data, cropped Sentinel-2 satellite imagery (for training location IDs 01, 02, 12 and 15, and validation locations near IDs 02 and 15) and also the triangulated polygons.</li> <li>Models.zip: pre-trained random forest and U-Net models based on the data, which can be generated by the Jupyter notebooks.<br> </li> </ul>
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.