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390 results for “template”
FIGURE 7. G. s p e l a e u s n in The first troglobitic Glomeridesmus from Brazil, and a template for a modern taxonomic description of Glomeridesmida (Diplopoda)
FIGURE 7. G. s p e l a e u s n. sp., paratype male (ZFMK Myr0936), SEM. A: last pair of walking legs (34) in oral view with sternite and subanal plate; B: anal shield, dorsal view, line marks part covered by last tergite (19). Abbreviations: Cx-St? = coxosternite; F = femur; poF = postfemur; prF = prefemur; s = field of small setae; S = isolated long setae, sp = spine-like claw; St = sternite; sub = subanal plate. Scale bars = 200 µm.
FIGURE 4. G. s p e l a e u s n in The first troglobitic Glomeridesmus from Brazil, and a template for a modern taxonomic description of Glomeridesmida (Diplopoda)
FIGURE 4. G. s p e l a e u s n. sp., A–D paratype male (ZFMK Myr0936), E paratype female (ISLA 3838) SEM. A: leg pairs 4–7 with latero-tergites; B: coxosternite 6 with stigma opening; C: coxal pouch 7; D: midbody latero-tergite of male; E: endbody latero-tergite female. Abbreviations: cp = coxal pouch; Cx-St = coxosternite; F = femur; LT = latero-tergite; poF = postfemur; prF = prefemur; st = stigma; Ta = tarsus; Ti = Tibia; numbers refer to leg pair number. Scale bars: A = 500 µm; B = 80 µm; C = 30 µm; D = 100 µm; E = 100 µm.
FIGURE 3. G. s p e l a e u s n in The first troglobitic Glomeridesmus from Brazil, and a template for a modern taxonomic description of Glomeridesmida (Diplopoda)
FIGURE 3. G. s p e l a e u s n. sp., paratype female (ISLA 3837), SEM. A: midbody segment, anterior (oral) view; B: coxosternites and prefemur of 2nd leg with ovipositor, posterior view; C: head, ventral view; D: right organ of Tömösváry, lateral view; E: head, dorsal view; F: antenna, antennomere 7 and disc, lateral view; G: gnathochilarium, underside. Abbreviations: C&P = tarsal claw and paranychium; Ca = cardines of gnathochilarium; Ca1 = cardo, basal mandible joint 1; Ca2? = either cardo (joint 1) or stipites (joint 2) of mandible; cP = central pads, modified central palpi; Cx-St = coxosternite; F = femur; Gn = gnathochilarium; iP = inner palpi; La = labrum; LL = lamella linguales; LP = lateral palpi; LT = latero-tergite; Me = mentum; O = ovipositor; Pl = plate covering organ of TO; poF = postfemur; prF = prefemur; St = stipes of gnathochilarium; sc = apical cones; St? = sternite?; T = tergite; Ta = tarsus; Ti = Tibia; TO = organ of Tömösváry; roman numerals refer to number of antennomeres. Scale bars: A = 200 µm; B = 100 µm; C = 200 µm; D = 20 µm; E = 200 µm; F = 20 µm; G = 100 µm.
FIGURE 1 in The first troglobitic Glomeridesmus from Brazil, and a template for a modern taxonomic description of Glomeridesmida (Diplopoda)
FIGURE 1. Distribution map of Glomeridesmus spelaeus n. sp.. Stars and SL # refer to caves. Specimens were found yearround in SL31 (type locality), in the other caves only during wet season.
FIGURE 2 in The first troglobitic Glomeridesmus from Brazil, and a template for a modern taxonomic description of Glomeridesmida (Diplopoda)
FIGURE 2. Glomeridesmus spelaeus, living specimens. First published pictures of living Glomeridesmida. arrows point to eggs and and intestine. Note the large eggs. Intestine not straight tube but folded.
All Spectra Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>Zip files of all the individual SDSS BOSS spectra. They are sorted by metallicity, luminosity class and spectral type, where each zip file contains all the original SDSS spectra to be co-added into each template. The fits files are in the following format: The primary HDU contains comments about the right ascension, declination and other useful object information. The first table extension contains the spectrum information (wavelength, flux, inverse variance, etc.) The second table extension contains other parameter information (object type, flags, and more). </p> <p> </p>
Conception and Design of Privacy-preserving Software Architecture Templates - Study Data & Questionnaires
<p>Resulting study data and used questionnaires of the expert-interview in the evaluation of privacy templates. This is part of the bachelor's thesis of Nikolai Prjanikov on the topic of "Conception and Design of Privacy-preserving Software Architecture Templates". </p>
Data for: Template-Directed Synthesis of Strained meso-meso-Linked Porphyrin Nanorings
<p>Calculated molecular xyz coordinates from molecular dynamics simulations, StrainViz calculations and screening of ligand designs.</p>
Library templates
Open the record for dataset details and reuse information.
Msfinder templates
Open the record for dataset details and reuse information.
Highly porous scaffolds for Ru-based microsupercapacitor electrodes using hydrogen bubble templated electrodeposition
<p>Data that supports the plots of Energy Storage Materials 47 (2022) 134–140</p>
Dataset - Templates Recommendation in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing templates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>Two approaches have been trained on this dataset in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>, namely a Natural Language Inference (NLI) approach based on SciBERT embeddings and an unsupervised approach based on ElasticSearch.</p> <p> </p> <p>This publication consists therefore of one general dataset, two training sets for each approach, validation set for the supervised approach and a test set for both approaches.</p> <p> </p> <p><strong>dataset.json</strong></p> <p>The main JSON object consists of a list of templates and a list of neutral papers.</p> <p>Each template object has an ID, label, list of research fields, list of properties and list of papers using that template, whereas each paper object has ID, label, DOI, research field and abstract.</p> <p>Each neutral paper object has the same schema of a paper object using that template.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "templates": [ { "id": "R138668", "label": "Psychiatric Disorders AI Overview", "research_fields": [ { "id": "http://orkg.org/orkg/resource/R133", "label": "Artificial Intelligence" } ... ], "properties": [ "Study cohort", ... ], "papers": [ { "id": "R138698", "label": "Application of Autoencoder in Depression Diagnosis", "doi": "10.12783/dtcse/csma2017/17335", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Major depressive disorder (MDD) is a mental disorder characterized by at least two weeks of low mood which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. No method can automatically extract discriminative features from the origin time series in fMRI images for MDD diagnosis. In this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge. An autoencoder was used to learn pre-training parameters of a dimensionality reduction process using 3-D convolution network. Through comparison with the other three feature extraction methods, our method achieved the best classification performance. This method can be used not only in MDD diagnosis, but also other similar disorders." }, ... }, ... ] "neutral_papers": [ { "id": "R109377", "label": "Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants", "doi": "10.1016/j.jpha.2020.03.009", "research_field": { "id": "R104", "label": "Bioinformatics" }, "abstract": "Abstract The recent outbreak of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 in December 2019 raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and middle east respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19." }, ... ] }</code></pre> <p> </p> <p><strong>All other files</strong></p> <p>The main JSON object consists of a list of entailments, a list of contradiction and a list of neutrals.</p> <p>Each object of the above mentioned lists has the same schema. An instance_id created by concatenating the template_id (when exists) with the paper_id, a template_id, a paper_id, premise (representing the paper's title), hypthesis (representing the paper's abstract), their concatenation in sequence and the target class.</p> <p>See an example instance below.</p> <p> </p> <pre><code class="language-json">{ "entailments": [ { "instance_id": "R138668xR138698", "template_id": "R138668", "paper_id": "R138698", "premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data", "hypothesis": "application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders", "sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] application of autoencoder in depression diagnosis major depressive disorder (mdd) is a mental disorder characterized by at least two weeks of low mood which is present across most situations diagnosis of mdd using rest state functional magnetic resonance imaging (fmri) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability no method can automatically extract discriminative features from the origin time series in fmri images for mdd diagnosis in this study, we proposed a new method for feature extraction and a workflow which can make an automatic feature extraction and classification without a prior knowledge an autoencoder was used to learn pre training parameters of a dimensionality reduction process using 3 d convolution network through comparison with the other three feature extraction methods, our method achieved the best classification performance this method can be used not only in mdd diagnosis, but also other similar disorders [SEP]", "target": "entailment" }, ... ], "contradictions": [ ... ], "neutrals": [ ... ] } </code></pre> <p> </p> <p><strong>Statistics</strong></p> <table align="center"> <tbody> <tr> <td>-</td> <td><strong>Training (supervised)</strong></td> <td><strong>Validation (supervised)</strong></td> <td><strong>Training (unsupervised)</strong></td> <td><strong>Test</strong></td> </tr> <tr> <td>Entailment</td> <td>180</td> <td>20</td> <td>200</td> <td>52</td> </tr> <tr> <td>Neutral</td> <td>180</td> <td>20</td> <td>200</td> <td>64</td> </tr> <tr> <td>Contradictrion</td> <td>736</td> <td>84</td> <td>0</td> <td>0</td> </tr> <tr> <td>Total</td> <td>1096</td> <td>124</td> <td>400</td> <td>116</td> </tr> </tbody> </table> <p> </p>
Supporting Information videos: Ice-Template Crosslinked PVA Aerogels Modified with Tannic Acid and Sodium Alginate
<p>Vertical burning videos of different aerogels:</p> <p>Video S1: P5 aerogel during vertical burning.</p> <p>Video S2: P5T3A3 uncrosslinked aerogel during vertical burning.</p> <p>Video S3 and S4: Self-extinguished performance of P5T3A3 crosslinked aerogel.</p> <p> </p>
Assessing user stories: the influence of template differences and gender-related problem-solving styles - Supplemental material
<p>Here we include the supplementary material that may be used as a replication package for the quasi-experiment reported in the paper "Assessing user stories: the influence of template differences and gender-related problem-solving styles", submitted to REJ Special Issue - RE 2021.</p>
SNID template data set v1.0.0
<p>Original version of the SNID template data set distributed with snid-5.0.</p> <p> Reading template files...agn kcE kcS0 kcSB1 kcSB2 kcSB3 kcSB4 kcSB5 kcSB6 kcSa kcSb kcSc lbv01ac lbv03hy lbv99bw mstar sn00E sn00H sn00cx sn01el sn02ap sn02bo sn02cx sn02er sn02ic sn03cg sn03du sn03fg sn04S sn04aw sn04dj sn04eo sn04et sn05bf sn05cs sn05gj sn05hj sn05hk sn05kl sn06aj sn06bp sn06gz sn79C sn80K sn81B sn83N sn83V sn84L sn86G sn87A sn88L sn89B sn90B sn90I sn90K sn90N sn90O sn90U sn90aa sn91A sn91M sn91N sn91T sn91ar sn91bg sn92A sn92H sn92ar sn93J sn93ac sn94D sn94I sn94M sn94Q sn94S sn94T sn94ae sn95D sn95E sn95F sn95ac sn95al sn95bd sn96C sn96L sn96X sn96cb sn97br sn97cn sn97cy sn97dc sn97dd sn97dq sn97ef sn97ei sn98S sn98T sn98aq sn98bu sn98bw sn98dt sn99aa sn99ac sn99aw sn99by sn99di sn99dn sn99ee sn99em sn99ex sn99gi done<br> Loaded 1515 spectra out of 111 templates</p>
SNID template data set v2.0.0
<div>New version (2.0.0) of the SNID template data set. This should be used in place of the v1.0.0 data set with snid-5.0.</div> <div> </div> <div> Reading template files...agn kcE kcS0 kcSB1 kcSB2 kcSB3 kcSB4 kcSB5 kcSB6 kcSa kcSb kcSc lbv1999bw lbv2001ac lbv2003hy mstar sn1979C sn1980K sn1981B sn1983N sn1983V sn1984A sn1984L sn1986G sn1987A sn1988L sn1989B sn1990B sn1990I sn1990K sn1990N sn1990O sn1990U sn1990aa sn1991A sn1991M sn1991N sn1991T sn1991ar sn1991bg sn1992A sn1992H sn1992ar sn1993J sn1993ac sn1994D sn1994I sn1994M sn1994Q sn1994S sn1994T sn1994ae sn1995D sn1995E sn1995F sn1995ac sn1995ak sn1995al sn1995bd sn1996C sn1996L sn1996X sn1996Z sn1996ab sn1996ai sn1996bk sn1996bl sn1996bo sn1996bv sn1996cb sn1997E sn1997Y sn1997bp sn1997bq sn1997br sn1997cn sn1997cy sn1997dc sn1997dd sn1997do sn1997dq sn1997dt sn1997ef sn1997ei sn1998S sn1998T sn1998V sn1998ab sn1998aq sn1998bp sn1998bu sn1998bw sn1998co sn1998de sn1998dh sn1998dk sn1998dm sn1998dt sn1998dx sn1998ec sn1998ef sn1998eg sn1998es sn1999X sn1999aa sn1999ac sn1999aw sn1999bh sn1999by sn1999cc sn1999cl sn1999cp sn1999cw sn1999da sn1999di sn1999dn sn1999dq sn1999ee sn1999ef sn1999ej sn1999ek sn1999em sn1999ex sn1999gd sn1999gh sn1999gi sn1999gp sn2000B sn2000E sn2000H sn2000bh sn2000bk sn2000ce sn2000cf sn2000cn sn2000cp sn2000cu sn2000cw sn2000cx sn2000dg sn2000dk sn2000dm sn2000dn sn2000fa sn2001E sn2001G sn2001N sn2001V sn2001ah sn2001ay sn2001az sn2001bf sn2001bg sn2001br sn2001cj sn2001ck sn2001cp sn2001da sn2001eh sn2001el sn2001en sn2001ep sn2001ex sn2001fe sn2001fh sn2001gc sn2002G sn2002ap sn2002aw sn2002bf sn2002bo sn2002cd sn2002cf sn2002ck sn2002cr sn2002cs sn2002cu sn2002cx sn2002de sn2002dj sn2002dl sn2002do sn2002dp sn2002ef sn2002er sn2002es sn2002eu sn2002fb sn2002fk sn2002ha sn2002hd sn2002he sn2002hu sn2002hw sn2002ic sn2002jg sn2002jy sn2002kf sn2003U sn2003W sn2003Y sn2003bg sn2003cg sn2003ch sn2003cq sn2003du sn2003fa sn2003gn sn2003hu sn2003hv sn2003ic sn2003it sn2003iv sn2003kc sn2003kf sn2004L sn2004S sn2004as sn2004at sn2004aw sn2004bd sn2004bg sn2004bk sn2004dj sn2004dt sn2004ef sn2004eo sn2004et sn2004fu sn2004fz sn2004gc sn2004gs sn2005A sn2005M sn2005am sn2005bc sn2005be sn2005bf sn2005bl sn2005bo sn2005cc sn2005cf sn2005cg sn2005cs sn2005el sn2005eq sn2005eu sn2005gj sn2005hc sn2005hf sn2005hj sn2005hk sn2005iq sn2005kc sn2005ke sn2005ki sn2005kl sn2005ls sn2005lu sn2005mc sn2005mz sn2005na sn2006D sn2006H sn2006N sn2006S sn2006X sn2006ac sn2006aj sn2006ak sn2006al sn2006ax sn2006az sn2006bp sn2006bq sn2006br sn2006bt sn2006bw sn2006bz sn2006cc sn2006cf sn2006cj sn2006cm sn2006cp sn2006cq sn2006cz sn2006em sn2006eq sn2006et sn2006eu sn2006ev sn2006gj sn2006gr sn2006gt sn2006gz sn2006hb sn2006kf sn2006le sn2006lf sn2006mo sn2006nz sn2006oa sn2006ot sn2006sr sn2006te sn2007A sn2007F sn2007S sn2007Y sn2007ae sn2007af sn2007al sn2007ap sn2007au sn2007ax sn2007ba sn2007bc sn2007bd sn2007bj sn2007bm sn2007bz sn2007ca sn2007cg sn2007ci sn2007co sn2007cq sn2007fb sn2007fs sn2007hj sn2007if sn2007jg sn2007kk sn2007le sn2007nq sn2007qe sn2007sr sn2007ux sn2008A sn2008C sn2008D sn2008L sn2008Q sn2008R sn2008Z sn2008ae sn2008af sn2008ar sn2008bf snls03D3bb done<br> Loaded 3754 spectra out of 349 templates</div>
Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses: Raw Data and TAP Template
<p>This is a data set and TAP template to accompany the paper</p> <p>"Technology Acquisition Plans to Foster<span> </span>Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses" by Tenbergen and Mead published at the 36th International Conference on Software Engineering Education and Training.</p>
SNID template data set v2.0.0 extended to 2 microns (1024 and 2048 wavelength bins)
<p>This set of SNID templates extends the v2.0.0 set out to 2 microns (wavelength range 2500 Ang-2 microns). It should be used when the input spectrum has data extending beyond 1 micron, since the original templates-2.0 set only covers 2500-10000 Ang.</p> <p>Two versions of the data set are published, one with 1024 wavelength bins, the other with 2048 wavelength bins.</p> <p> Reading template files...sn1979C sn1980K sn1981B sn1983N sn1983V sn1984A sn1984L sn1986G sn1987A sn1988L sn1989B sn1990B sn1990I sn1990K sn1990N sn1990O sn1990U sn1990aa sn1991A sn1991M sn1991N sn1991T sn1991ar sn1991bg sn1992A sn1992H sn1992ar sn1993J sn1993ac sn1994D sn1994I sn1994M sn1994Q sn1994S sn1994T sn1994ae sn1995D sn1995E sn1995F sn1995ac sn1995ak sn1995al sn1995bd sn1996C sn1996L sn1996X sn1996Z sn1996ab sn1996ai sn1996bk sn1996bl sn1996bo sn1996bv sn1996cb sn1997E sn1997Y sn1997bp sn1997bq sn1997br sn1997cn sn1997cy sn1997dc sn1997dd sn1997do sn1997dq sn1997dt sn1997ef sn1997ei sn1998S sn1998T sn1998V sn1998ab sn1998aq sn1998bp sn1998bu sn1998bw sn1998co sn1998de sn1998dh sn1998dk sn1998dm sn1998dt sn1998dx sn1998ec sn1998ef sn1998eg sn1998es sn1999X sn1999aa sn1999ac sn1999aw sn1999bh sn1999by sn1999cc sn1999cl sn1999cp sn1999cw sn1999da sn1999di sn1999dn sn1999dq sn1999ee sn1999ef sn1999ej sn1999ek sn1999em sn1999ex sn1999gd sn1999gh sn1999gi sn1999gp sn2000B sn2000E sn2000H sn2000bh sn2000bk sn2000ce sn2000cf sn2000cn sn2000cp sn2000cu sn2000cw sn2000cx sn2000dg sn2000dk sn2000dm sn2000dn sn2000fa sn2001E sn2001G sn2001N sn2001V sn2001ah sn2001ay sn2001az sn2001bf sn2001bg sn2001br sn2001cj sn2001ck sn2001cp sn2001da sn2001eh sn2001el sn2001en sn2001ep sn2001ex sn2001fe sn2001fh sn2001gc sn2002G sn2002ap sn2002aw sn2002bf sn2002bo sn2002cd sn2002cf sn2002ck sn2002cr sn2002cs sn2002cu sn2002cx sn2002de sn2002dj sn2002dl sn2002do sn2002dp sn2002ef sn2002er sn2002es sn2002eu sn2002fb sn2002fk sn2002ha sn2002hd sn2002he sn2002hu sn2002hw sn2002ic sn2002jg sn2002jy sn2002kf sn2003U sn2003W sn2003Y sn2003bg sn2003cg sn2003ch sn2003cq sn2003du sn2003fa sn2003gn sn2003hu sn2003hv sn2003ic sn2003it sn2003iv sn2003kc sn2003kf sn2004L sn2004S sn2004as sn2004at sn2004aw sn2004bd sn2004bg sn2004bk sn2004dj sn2004dt sn2004ef sn2004eo sn2004et sn2004fu sn2004fz sn2004gc sn2004gs sn2005A sn2005M sn2005am sn2005bc sn2005be sn2005bf sn2005bl sn2005bo sn2005cc sn2005cf sn2005cg sn2005cs sn2005el sn2005eq sn2005eu sn2005gj sn2005hc sn2005hf sn2005hj sn2005hk sn2005iq sn2005kc sn2005ke sn2005ki sn2005kl sn2005ls sn2005lu sn2005mc sn2005mz sn2005na sn2006D sn2006H sn2006N sn2006S sn2006X sn2006ac sn2006aj sn2006ak sn2006al sn2006ax sn2006az sn2006bp sn2006bq sn2006br sn2006bt sn2006bw sn2006bz sn2006cc sn2006cf sn2006cj sn2006cm sn2006cp sn2006cq sn2006cz sn2006em sn2006eq sn2006et sn2006eu sn2006ev sn2006gj sn2006gr sn2006gt sn2006gz sn2006hb sn2006kf sn2006le sn2006lf sn2006mo sn2006nz sn2006oa sn2006ot sn2006sr sn2006te sn2007A sn2007F sn2007S sn2007Y sn2007ae sn2007af sn2007al sn2007ap sn2007au sn2007ax sn2007ba sn2007bc sn2007bd sn2007bj sn2007bm sn2007bz sn2007ca sn2007cg sn2007ci sn2007co sn2007cq sn2007fb sn2007fs sn2007hj sn2007if sn2007jg sn2007kk sn2007le sn2007nq sn2007qe sn2007sr sn2007ux sn2008A sn2008C sn2008D sn2008L sn2008Q sn2008R sn2008Z sn2008ae sn2008af sn2008ar sn2008bf snls03D3bb done<br> Loaded 4388 spectra out of 333 templates</p>
bOTTR: Batch Instantiation of OTTR templates
<div> <p>This specification defines <em>bOTTR</em>, a language for specifying mappings between queries over the sources to given templates. bOTTR hence allows multiple data sources on different formats to be integrated via OTTR templates into a single RDF/OWL representation.</p> <p>The vocabulary of the bOTTR language is specified by an OWL ontology which extends the wOTTR ontology.</p> </div>
Turn-on of a ruthenium complex photocatalysis by DNA-templated ligation - Raw data
<p>Raw data from analyses reported in the publication</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.