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23 results for “Sample digitization”
Widespread Sampling Biases in Herbaria Revealed from Large-Scale Digitization 1656-2016
Non-random collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in ~5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE) We identified numerous shared and unique biases among these regions. Shared biases included specimens i) collected close to roads and herbaria; ii) collected more frequently during spring; iii) of threatened species collected less frequently; and iv) of close relatives collected in similar numbers. Regional differences included i) over-representation of graminoids in SA and AU and of annuals in AU; and ii) peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by a few individuals. These mega-collectors, and their associated preferences and idiosyncrasies, may have shaped patterns of collection bias via ‘founder effects’. Studies using herbarium collections should account for sampling biases and future collecting efforts should avoid compounding these biases.
Supplemental catalogs for "The Sloan Digital Sky Survey Reverberation Mapping Project: Sample Characterization"
<p>We have compiled additional properties for the SDSS-RM sample in several ancillary catalogs. Below are the notes on these supplemental catalogs. There are .readme files for each additional catalog. We also include the quality assurance plots for the global spectral fits.</p> <p><strong>QA-0000-56837.ps.gz </strong>The full set of 849 quality assessment plots for the global spectral fitting. Each plot includes a top panel showing the continuum (brown) and Fe II (blue) model components; the red line is the sum of the two. The cyan diamonds are pixels masked as absorption or bad pixels. The gray brackets near the top of the panel indicate the windows used for the continuum+Fe II fit. The bottom panels present the emission line fits for five line complexes.</p> <p><strong>allqso_sdssrm.fits</strong> A FITS table of all 1214 known quasars in the 7 square degree SDSS-RM field. Only 849 of them received a fiber in the SDSS-RM spectroscopy. This table lists the basic target information of these quasars.</p> <p><strong>QSObased_Expanded_SDSSRM_107.fits</strong> The narrow MgII/FeII absorber catalog for SDSS-RM quasars, following the methodology outlined in Zhu & Ménard (2013). Each entry corresponds to one quasar. The search for narrow absorbers includes systems that have absorber redshift close to the quasar systemic redshift (|dz|<0.04). MgII absorbers blueshifted from the quasar by dz>0.04 and also redward of CIV by dz>0.02 are of high purity. MgII absorbers with |dz|<0.04 or those at wavelength blueward of CIV, or those with FeII detection but no MgII detections (likely due to bad pixels), while included in this catalog, should be treated with caution, and may contain a small fraction of false positives (mainly CIV absorbers).</p> <p>For convenience, we also provide a version of the absorber catalog organized by absorbers (<strong>Expanded_SDSSRM_107.fits</strong>), i.e., each entry corresponds to one absorber system.</p> <p><strong>rmqso32_aegis_multi_lambda.fits</strong> Multi-wavelength data compiled from Nandra et al. (2015) or 32 SDSS-RM quasars in the AEGIS field.</p> <p><strong>spitzer_seip_rm_match_1.5arcsec.fits</strong> Spitzer IRAC and MIPS data from the Spitzer Enhanced Imaging Products (SEIP) source list for 176 SDSS-RM quasars, with a matching radius of 1.5 arcseconds. This file also compiles infrared fluxes (if available) from 2MASS (Skrutskie et al. 2006).</p> <p><strong>spec_2014_BALrobust.csv</strong> List of 95 BALQSOs (including mini-BALQSOs) identified from the first-year coadded spectroscopy. This file includes BAL flags on CIV, AlIII, MgII, and FeII/FeIII. It also includes notes on individual objects.</p> <p><strong>PS1_MD07_LC_sdssrm.fits</strong> PS1 Medium Deep light curves for the SDSS-RM quasars used to compute PS1_NMAG_OK and PS1_RMS_MAG in the main catalog. Note this is the unofficial release of the PS1 MD07 data, which was approved by the PS1 collaboration. These photometric light curves may differ slightly from the final official release of the PS1 Medium Deep field data. </p>
Digital Elevation Model (DEM) of northern Brøggerhalvøya (Svalbard, Norway) with Ground Sampling Distance (GSD) of 50 cm
<p>HRSC is a multisensor pushbroom instrument with 9 CCD line sensors mounted in parallel that has been in orbit around Mars since January 2004 on ESA’s Mars Express spacecraft (Gwinner et al., 2016). It simultaneously obtains high-resolution stereo, multicolor, and multiphase images. Digital photogrammetric techniques are used to reconstruct the topography on the basis of five stereo channels, which provide five different views of the ground.</p> <p>An airborne version of the HRSC was used for the acquisition of stereo and color images in Svalbard. Since 1997, different airborne versions of HRSC have been developed. The principles of HRSC-AX data processing are described by Gwinner et al. (2006). The orientation data of the camera are reconstructed from a global positioning system inertial navigation system (GPS INS). HRSC-AX has been applied in diverse technical and scientific applications (e.g., Gwinner et al., 1999, 2000; Hauber et al., 2001; Otto et al., 2007) and has also been successfully used to investigate rock glacier activity (Roer and Nyenhuis, 2007). The flight campaign in July–August 2008 covered a total of seven regions in Svalbard: (1) Longyearbyen and the surroundings of Adventfjorden, (2) large parts of Adventdalen, (3) large parts of the Brøggerhalvøya (halvøya = peninsula) in western Spitsbergen (this dataset), (4) the Bockfjorden area in northern Spitsbergen, (5) the northeastern shore of the Palanderbukta and the margin of the adjacent ice cap in Nordaustlandet, (6) an area on Prins Karls Forland, and (7) the area of the abandoned Russian mining settlement of Pyramiden together with the nearby Ebbedalen. </p> <p>This dataset is a Digital Elevation Model (DEM) derived from HRSC-AX stereo images. The elevations recorded in the DEM are ellipsoid heights; i.e., they are not computed with respect to a geoid but to a mathematically defined reference surface, which is a<br>rotational ellipsoid with the equatorial A and B axes both having a radius of 6378.14 km and the polar<br>C axis having a radius of 6356.75 km. This results in an offset of about 36.5m with respect to geoid<br>heights; i.e., sea level in the HRSC-AX DEM is not at 0 m, but at ~36.5 m.</p> <p><strong>References</strong></p> <p>Gwinner, K., Hauber, E., Hoffmann, H., Scholten, F., Jaumann, R., Neukum, G.,<br>Coltelli, M., and Puglisi, G., 1999, The HRSC-A experiment on high reso-<br>lution imaging and DEM generation at the Aeolian Islands, in Proceedings<br>of the 13th International Conference on Applied Geologic Remote Sens-<br>ing: Ann Arbor, Michigan, ERIM International, v. I, p. 560–569.</p> <p>Gwinner, K., Hauber, E., Jaumann, R., and Neukum, G., 2000, High-resolution,<br>digital photogrammetric mapping: A tool for earth science: Eos<br>(Transactions, American Geophysical Union), v. 81, no. 44, p. 513–520,<br>doi:10.1029/00EO00364.</p> <p>Gwinner, K., Coltelli, M., Flohrer, J., Jaumann, R., Matz, K.-D., Marsella, M.,<br>Roatsch, T., Scholten, F., and Trauthan, F., 2006, The HRSC-AX Mt.<br>Etna Project: High-Resolution Orthoimages and 1 m DEM at Regional<br>Scale: International Archives of Photogrammetry and Remote Sensing,<br>v. XXXVI, Part 1, http://isprs.free.fr/documents/Papers/T05-23.pdf.</p> <p>Gwinner, K., Scholten, F., Spiegel, M., Schmidt, R., Giese, B., Oberst,<br>J., Heipke, C., Jaumann, R., and Neukum, G., 2009, Derivation and<br>validation of high-resolution digital elevation models from Mars Express<br>HRSC data: Photogrammetric Engineering and Remote Sensing, v. 75,<br>no. 9, p. 1127–1142.</p> <p>Gwinner, K., Jaumann, R., Hauber, E., et al., 2016, The High Resolution Stereo Camera (HRSC) of Mars Express and its<br>approach to science analysis and mapping for Mars and its satellites: Planetary and Space Science, v. 126, p. 93–138. http://dx.doi.org/10.1016/j.pss.2016.02.014</p> <p>Hauber, E., Slupetzky, H., Jaumann, R., Wewel, F., Gwinner, K., and Neukum,<br>G., 2001, Digital and automated high resolution stereo mapping of the<br>Sonnblick glacier: EARSeL eProceedings, v. 1, no. 1, p. 246–254.</p> <p>Jaumann, R., Neukum, G., Behnke, T., Duxbury, T.C., Eichentopf, K., Flohrer,<br>J., van Gasselt, S., Giese, B., Gwinner, K., Hauber, E., Hoffmann, H., Hoff-<br>meister, A., Köhler, U., Matz, K.-D., McCord, T.B., Mertens, V., Oberst,<br>J., Pischel, R., Reiss, D., Ress, E., Roatsch, T., Saiger, P., Scholten, F.,<br>Schwarz, G., Stephan, K., Wählisch, M., and the HRSC Co-Investigator<br>Team, 2007, The high-resolution stereo camera (HRSC) experiment on<br>Mars Express: instrument aspects and experiment conduct from interplan-<br>etary cruise through the nominal mission: Planetary and Space Science, v.<br>55, p. 928–952, doi:10.1016/j.pss.2006.12.003.</p> <p>Otto, J.-C., Kleinod, K., König, O., Krautblatter, M., Nyenhuis, M., Roer,<br>I., Schneider, M., Schreiner, B., and Dikau, R., 2007, HRSC-A data:<br>A new high-resolution data set with multipurpose applications in physi-<br>cal geography: Progress in Physical Geography, v. 31, no. 2, p. 179–197,<br>doi:10.1177/0309133307076479.</p> <p>Roer, I., and Nyenhuis, M., 2007, Rockglacier activity studies on a regional<br>scale: Comparison of geomorphological mapping and photogrammetric<br>monitoring: Earth Surface Processes and Landforms, v. 32, p. 1747–1758,<br>doi:10.1002/esp.1496.</p>
Digital Forestry Toolbox - Sample data
<p>This repository contains airborne laser scanning data samples acquired by the states of Geneva, Solothurn and Zurich (in Switzerland). They are used in the tutorials of the <a href="https://github.com/mparkan/Digital-Forestry-Toolbox">Digital Forestry Toolbox for Matlab/Octave</a>.</p> <p>The full datasets (covering the complete state extents) are available from here: </p> <ul> <li><a href="https://maps.zh.ch/">https://maps.zh.ch/</a></li> <li><a href="https://geoweb.so.ch/map/lidar">https://geoweb.so.ch/map/lidar</a></li> <li><a href="https://ge.ch/sitg/donnees">https://ge.ch/sitg/donnees</a></li> </ul> <p><strong>Sources</strong> <strong>and usage conditions</strong>:</p> <ul> <li>Canton de Genève, Département de l'aménagement, du logement et de l'énergie (DALE), Système d'information du territoire à Genève (SITG). Dataset extracted on December 6, 2018. <a href="https://ge.ch/sitg/media/sitg/files/documents/conditions_generales_dutilisation_des_donnees_et_produits_du_sitg_en_libre_acces.pdf">See usage conditions</a>.</li> <li>Kanton Zürich, <a href="https://are.zh.ch/internet/baudirektion/are/de/aktuell.html">Baudirektion, Amt für Raumentwicklung</a>. Dataset extracted on December 6, 2018. <a href="https://are.zh.ch/internet/baudirektion/are/de/geoinformation/geodaten_uebersicht/Open_Data_Kanton_Zuerich.html#datenbezug">See usage conditions</a>.</li> <li>Kanton Solothurn, <a href="https://www.so.ch/verwaltung/bau-und-justizdepartement/">Bau- und Justizdepartement, Amt für Geoinformation</a>. Dataset extracted on December 6, 2018. <a href="https://geoweb.so.ch/geodaten/index.php?action=nutzung&UID=&USR=&user_id=&lang=de&menue=&aktuell=&sogis_zip_ie=">See usage conditions</a>.</li> </ul>
Assessment of Usability and Preliminary Effectiveness of a Digital Sleep Aid in an Italian Sample
ClinicalTrials.gov study NCT06926348. IPD Sharing: NO. Countries: 1. Publications: 30.
Assessing the feasibility of a rapid, high-volume cervical cancer screening program using HPV self-sampling and digital colposcopy in rural regions of Yunnan, China
<p>Objective: Implementation of a novel, rapid, high-volume, see-and-treat cervical cancer screening program utilizing self-swab HPV testing and digital colposcopy in underserved regions of Yunnan China.</p> <p>Design: 500-1000 women per day self-swabbed for high-risk HPV (hrHPV+). Four careHPVTM machines (Qiagen) were run simultaneously to test the specimens. All hrHPV+ patients were contacted the same day and digital colposcopy (DC) was performed with the EVA system (MobileODT). Digital images were obtained, and all suspected lesions were biopsied and then treated.</p> <p>Setting: Rural and underserved areas of the Yunnan province, Kunming municipality.</p> <p>Participants: 3600 women, mean age 50.2 years, who had never been screened for cervical cancer. The women were of the Yi, Hui, Dai, and Han ethnicities.</p> <p>Interventions: Cryotherapy was performed on all lesions suspicious for CIN1 and LEEP was performed on all lesions suspicious CIN2+. ECC was performed if the transformation zone was not fully visualized.</p> <p>Results: 216 women (6%) were hrHPV+. 168 underwent same-day colposcopy (23 CIN1, 17 CIN2+). DC was able to identify 15 of 16 (93.8%) CIN2+ lesions.</p> <p>Conclusions: Currently, China does not have national screening guidelines or services for cervical cancer. Only 20.7% of Chinese women reported having ever been screened.</p> <p>This study illustrates a cost- effective, rapid and practical strategy that can be used to screen-and-treat 400+ million Chinese women for cervical cancer. First, HPV self-sampling allows large numbers of women to be screened rapidly and inexpensively and only +hrHPV women require further evaluation. Digital colposcopy is then performed on +hrHPV women with a portable digital colposcope. The high-resolution images obtained during DC then facilitates appropriate same-day treatment as they are able to accurately distinguish between CIN1 and CIN2+ lesions. Additionally, DC adds little to the time and cost of screening and produces permanent images that can be used for documentation, quality control, and expert consultation via a secure cloud-based portal.</p>
Using Delaunay triangulation to sample whole-specimen color from digital images
<p>1. Color variation is one of the most obvious examples of variation in nature, but biologically meaningful quantification and interpretation of variation in color and complex patterns is challenging. Many current methods for assessing variation in color patterns classify color patterns using categorical measures, provide aggregate measures that ignore spatial pattern, or both, losing potentially important aspects of color pattern.</p> <p>2. Here, we present Colormesh, a novel method for analyzing complex color patterns that offers unique capabilities. Our approach is based on unsupervised color quantification combined with geometric morphometrics to identify regions of putative spatial homology across samples, from histology sections to whole organisms. Colormesh quantifies color at individual sampling points across the whole sample.</p> <p>3. We demonstrate the utility of Colormesh using digital images of Trinidadian guppies (Poecilia reticulata), for which the evolution of color has been frequently studied. Guppies have repeatedly evolved in response to ecological differences between up- and downstream locations in Trinidadian rivers, resulting in extensive parallel evolution of many phenotypes. Previous studies have, for example, compared the area and quantity of discrete color (e.g., area of orange, number of black spots) between these up- and downstream locations neglecting spatial placement of these areas. Using the Colormesh pipeline, we show that patterns of whole-animal color variation do not match expectations suggested by previous work.</p> <p>4. Colormesh can be deployed to address a much wider range of questions about color pattern variation than previous approaches. Colormesh is thus especially suited for analyses that seek to identify the biologically important aspects of color pattern when there are multiple competing hypotheses, or even no a priori hypotheses at all.</p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
the area between the left femur and left manual digits. Arrows denote the positions of the samples. Scale bars, 2 µm. in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
the area between the left femur and left manual digits. Arrows denote the positions of the samples. Scale bars, 2 µm.
lil, left ilium;; lti, left tibia; pd, pedal digits; and ub, unidentified bony element. The white box indicates the position from which the sample was taken for histological analysis. Scale bars, 10 mm (a, c, d), 20 mm (b). in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
lil, left ilium;; lti, left tibia; pd, pedal digits; and ub, unidentified bony element. The white box indicates the position from which the sample was taken for histological analysis. Scale bars, 10 mm (a, c, d), 20 mm (b).
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
Assessing the feasibility of a rapid, high-volume cervical cancer screening program using HPV self-sampling and digital colposcopy in rural regions of Yunnan, China
Open the record for dataset details and reuse information.
Using Delaunay triangulation to sample whole-specimen color from digital images
Open the record for dataset details and reuse information.
Sample data for tech savvy digital media
Open the record for dataset details and reuse information.
Digital Analysis of Primary Teeth Crown Dimensions and Clinical Evaluation in a Sample of Egyptian Children
ClinicalTrials.gov study NCT06874218. IPD Sharing: NO. Countries: 1. Publications: 0.
Digital gene expression (DGE) sequencing of 10 pairs samples between kidney normal tissue and cancer tissue
GEO Series GSE24455. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Digital gene expression analysis of 5 rice samples
GEO Series GSE27240. Oryza sativa. 5 samples. Type: Expression profiling by high throughput sequencing.
High-throughput SuperSAGE for digital gene expression analysis of multiple samples using Next Generation Sequencing
GEO Series GSE20682. Arabidopsis thaliana; Oryza sativa; Danio rerio. 31 samples. Type: Expression profiling by high throughput sequencing.
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </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.