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2,774 results for “AD”
Exploring Economic Integration of Peasant Settlements Central Roman Spain (1st - 3rd c. AD). Dataset.
<p>Archaeological data used for the paper Exploring Economic Integration of Peasant Settlements in Roman Central Spain (1st - 3rd c. AD). The supplement is composed by three CSV files recording respectively the presence and frequency of an artefact chrono-type at a site, the adjacency matrix (MATRIX_SITE-CHRONOTYPE_CARPETANIA.csv), the attribute information per site (ATTRIB_SITES_CARPETANIA.csv) and the attribute information per artefact chrono-type (ATTRIB_CHRONOTYPES_CARPETANIA.csv). Also included are the raw .graphmlz files relating to the analyses carried out which include, among other data, the Louvain modularity. <br> In order to view and analyse the .graphmlz files provided in this archive, you can use the Visone software, a tool for visualizing and analyzing networks: To view these .graphmlz files, first download and install Visone software from their official website (<a href="https://visone.ethz.ch/">https://visone.ethz.ch/</a>). Then, download the .graphmlz files from the Zenodo archive. Open Visone, and navigate to 'File' > 'Open' to access the downloaded files.</p>
Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers
<p>This dataset contains the data used in the publication: <br> <strong><em>"Carnauba Wax and Beeswax as Structuring Agents for Water-in-Oleogel Emulsions without Added Emulsifiers</em>" </strong> (https://doi.org/10.3390/foods12091850)</p> <p> </p> <p> </p> <p> </p> <p>Important abbreviations: </p> <ul> <li>#CRW: Indicates the concentration of carnauba wax in the fat phase (e.g., 5CRW - indicates 5%w/w of carnauba wax in the fat phase)</li> <li>#BZW: Indicates the concentration of beeswax in the fat phase (e.g., 5BZW - indicates 5%w/w of beeswax in the fat phase)</li> <li>#W: The number preceding indicates the percentage of water in the sample (e.g., 40W indicates 40%w/w of water in the emulsion)</li> <li>t#: Indicates the number of days in storage (e.g. t1 indicates the sample was measured after 1 day of storage time)</li> <li>LS: Indicates that the sample was prepared on a lab scale</li> <li>PS: Indicates that the sample was prepared on a pilot scale</li> </ul> <p>This dataset contains: </p> <ul> <li>Microscopies <ul> <li>PLM <ul> <li><strong>Figure 6A</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6D</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7A</strong> - Pilot scale emulsion - 5% beeswax - 20% water</li> <li><strong>Figure 7D </strong>- Pilot scale oleogel - 5% beeswax<br> </li> <li><strong>Figure 8A </strong>- CRW-S - Lab scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8B - </strong>CRW-D<strong> - </strong>Pilot scale oleogel - 5% carnauba wax</li> <li><strong>Figure 8C - </strong>BZW-S<strong>- </strong>Lab scale oleogel - 5% beeswax</li> <li><strong>Figure 8D</strong> - BZW-D - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>Cryo-SEM <ul> <li><strong>Figure 6B </strong>- Pilot scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6E</strong> - Pilot scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7B</strong> - Pilot scale emulsion - 5% beeswax - 20% water </li> <li><strong>Figure 7E</strong> - Pilot scale oleogel - 5% beeswax<br> </li> </ul> </li> <li>CLSM <ul> <li><strong>Figure 6C</strong> - Lab scale emulsion - 5% carnauba wax - 40% water </li> <li><strong>Figure 6F</strong> - Lab scale oleogel - 5% carnauba wax<br> </li> <li><strong>Figure 7C</strong> - Lab scale emulsion - 5% beeswax - 40% water</li> <li><strong>Figure 7F </strong>- Lab scale oleogel - 5% beeswax <ul> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Rheology data <ul> <li>Strain sweeps <ul> <li><strong>LS_2.5CRW_20W_t1.txt</strong> - Lab scale emulsion - 2.5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_0W_t1.txt</strong> - Lab scale oleogel - 5% carnauba wax - day 1</li> <li><strong>LS_5CRW_20W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>LS_5CRW_30W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 30% water - day 1</li> <li><strong>LS_5CRW_40W_t1.txt</strong> - Lab scale emulsion - 5% carnauba wax - 40% water - day 1</li> <li><strong>LS_7.5CRW_20W_t1.txt</strong> - Lab scale emulsion- 7.5% carnauba wax - 20% water - day 1<br> </li> <li><strong>LS_1.5BZW_20W_t1.txt</strong> - Lab scale emulsion - 1.5% beeswax - 20% water - day 1</li> <li><strong>LS_2.25BZW_20W_t1.txt</strong> - Lab scale emulsion - 2.25% beeswax - 20% water - day 1 </li> <li><strong>LS_5BZW_0W_t1.txt</strong> - Lab scale oleogel - 5% beeswax - day 1</li> <li><strong>LS_5BZW_20W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>LS_5BZW_30W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 30% water - day 1</li> <li><strong>LS_5BZW_40W_t1.txt</strong> - Lab scale emulsion - 5% beeswax - 40% water - day 1<br> </li> <li><strong>PS_5BZW_0W_t1.txt</strong> - Pilot scale oleogel - 5% beeswax - day 1</li> <li><strong>PS_5BZW_20W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 20% water - day 1</li> <li><strong>PS_5BZW_40W_t1.txt</strong> - Pilot scale emulsion - 5% beeswax - 40% water - day 1</li> <li><strong>PS_5CRW_0W_t1.txt</strong> - Pilot scale oleogel - 5% carnauba wax - day 1</li> <li><strong>PS_5CRW_20W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 20% water - day 1</li> <li><strong>PS_5CRW_40W_t1.txt</strong> - Pilot scale emulsion - 5% carnauba wax - 40% water - day 1<br> </li> </ul> </li> <li>Data compilation: <ul> <li><strong>Rheology - Average.csv</strong><br> Please consider the following abbreviations in the file <ul> <li>CGC = Critical gelling concentration</li> <li>G' = Storage modulus</li> <li>G'' = Loss modulus</li> <li>G* = Complex modulus</li> <li>Strain LVE = Yield strain </li> <li>COP = Crossover point</li> <li>NaN = indicates that the system was not stable and thus not measured</li> <li>Data is presented as Avgerave ± Standard Deviation (of the three repetitions)</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> </ul>
Translation and references to commentary of Maximianus' Elegia Prima (6th c. AD)
<p>This dataset pertains to the article "Laat of te Laat? Ouderdom in de laat-Latijnse Elegieën van Maximianus (6de eeuw n.Chr.)" (related to <strong>FWO grant 1107123N</strong>) and contains the following:</p> <ol> <li>Latin text of Maximianus' Elegia Prima (edition by D'Amanti 2020)</li> <li>English translation (my own)</li> <li>Source reference</li> <li>Reference to commentary on Latin text (D'Amanti 2020)</li> </ol> <p>Physical copies supplementing this data are the following (ISBN in related identifiers):</p> <ul> <li>D'Amanti, E.R. (2020). Massimiano: Elegie. Fondazione Lorenzo Valla.</li> <li>Franzoi, A. (2014). Le Elegie di Massimiano. Adolf M. Hakkert. </li> </ul>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Forensic Exchange Analysis of Contact Artifacts on Data Hiding Timestamps-ADS Experiment Supplementary Files
<p>Da-Yu Kao is an Associate Professor at the Department of Information Management, Central Police University, Taiwan. He was a detective and forensic police officer at Taiwan's Criminal Investigation Bureau (under the National Police Administration). With a Master's degree in Information Management and a Ph.D. degree in Crime Prevention and Correction, he had led several investigations in cooperation with police agencies from other countries for the past 20 years. He is now the director of Computer Crime Investigation Lab at Central Police University and the webmaster of Cybercrime Investigation and Digital Forensics in the Facebook Group.</p>
FIG. 7 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 7. — Proportion of animals targeted by hunting (grey), garden-hunting (black) or both methods (white) in each sites.
FIG. 6 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 6. — Hierarchical clustering of the: A, taxa; and B, sites analysed in the Canonical Analysis. Abbreviations: Aq., Aquatic animals; Can., Canids; Oth., Miscelanaous taxa; Exo., exotic animals; Ov., white-tailed deer; Art., other artiodactyls; Fel., felids; Sc., small carnivores; Lag., lagomorpha; Com., commensal animals; Tur, turkey; Rap., prey birds; Tiz., Tizayuca; Calix., Calixtlahuaca; Bar-Clas., Barajas Classic/Early Postclassic occupation; Bar-PCR, Barajas Late Postclassic occupation; E.S., El Salitre; Ang., Angamuco.
FIG. 5 in The use of animals in Northern Mesoamerica, between the Classic and the Conquest (200-1521 AD). An attempt at regional synthesis on central Mexico
FIG. 5. — Distribution of CA scores on: A, C1xC2 axes; and B, C1xC3 axes. Taxa bubbles surfaces represent their actual inertia in each plan. Sites and sup- plemental individuals are normalized to 1. Abbreviations: Aq., Aquatic animals; Can., Canids; Com., commensal animals; Ov., white-tailed deer; Tur, turkey.
ADS usage versus region GDP per capita (data)
<p>These files contain the data used for constructing figure 2 in "The ADS in the Information Age - Impact on Discovery" (http://adsabs.harvard.edu/abs/2012opsa.book..253H, arXiv:1106.5644). This figure shows the fraction of ADS world usage (for specific regions) as a function of GDP per capita (all values normalized by their 1997 value). For specifics: see paper. The data have been uploaded as a JPG figure, plain text file and a TAR archive with files used by the graphing program DataGraph (http://www.visualdatatools.com/DataGraph/).</p>
Famagusta, Cyprus. St. Nicholas, south side, inscription of AD 1311.
<p>Famagusta, Cyprus. St. Nicholas, inscription of AD 1311, on south side, on buttress next to south door, as documented in 1973.</p>
Added graph to previous Alsop data set
<p>A previous plot compared CHU 14.67 MHz Doppler shift between the day before and day of eclipse.</p> <p>I also had available Doppler shift data from 8/19 (two days before). I added that to the graph to perhaps help detect or dismiss a previously observed effect. The two days before the eclipse were pretty comparable from a Doppler Shift standpoint.</p>
ETCBC/bhsa: Added NER specs
<p>The TF feature data is identical to the previous release.</p> <p>What is addes is the directory <code>ner</code> at the toplevel. This contains the config and test spec for manual entity markup with the new annotate tool.</p>
Material adicional de Ex cenobio Sancti Ysidori Lengionensis usque ad Bibliothecam Regiam Belgicam: De partidas, cronicones y sermones romances
<p>Material adicional al artículo publicado en <i>Incipit</i> 43 (2023).</p>
Fig. 1 in Antioxidant activity of bee products added to water in tebuconazole-exposed fish
Fig. 1. Levels of TBARS (nmol MDA mg-1 protein) and GSH (µmol GSH g-1 of wet tissue) in Rhamdia quelen after exposure to 16.6% of LC 50 of tebuconazole, to bee product, and to tebuconazole + bee product for 96 h. Different small letters indicates statistical differences between the means (ANOVA followed by Tukey´s multiple range test). Mean ± SEM; n = 10. * P <0.05.
Significant sQTLs and splicing TWAS reference panels (AMP-AD brain and EADB Belgian LCL cohorts)
<p>This dataset is part of the manuscript "<em><strong>New insights into the genetic etiology of Alzheimer’s disease and related dementias</strong></em>" by Bellenguez, Küçükali, et al. Nature Genetics 2022.</p> <p>Publication link: <a href="https://www.nature.com/articles/s41588-022-01024-z">https://www.nature.com/articles/s41588-022-01024-z</a></p> <p>GitHub repository of all QTL/TWAS data shared for this study: <a href="https://github.com/SleegersLab-VIBCMN/EADB_GWAS_NatureGenetics_QTL_TWAS">https://github.com/SleegersLab-VIBCMN/EADB_GWAS_NatureGenetics_QTL_TWAS</a></p> <p>For details, please see the publication. For any questions, please contact Fahri Küçükali (<a href="mailto:fahri.kucukali@uantwerpen.vib.be">fahri.kucukali@uantwerpen.vib.be</a>) and Kristel Sleegers (<a href="mailto:Kristel.Sleegers@uantwerpen.vib.be">Kristel.Sleegers@uantwerpen.vib.be</a>).</p> <p>Significant sQTL catalogues are compressed with <em>gzip </em>and tar achieve of splicing TWAS reference panels are compressed with <em>bzip2</em>.</p> <p><strong>sQTL catalogues</strong></p> <p>The files show significant sQTL - splice junction pairs mapped in AMP-AD brain and EADB Belgian LCL cohorts. The catalogues are in hg38/GRCh38 human genome build. Most of the columns in the files are based on FastQTL output (<a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a>).</p> <p><em>sQTL file columns:</em></p> <ol> <li>variant_id - ID of the significant sQTL variant based on the dbSNPv151 rsID annotation or hg38/GRCh38 CHR_POS_REF_ALT ID if rsID not available.</li> <li>junc_id - sJunction ID assigned by Regtools/Leafcutter pipeline. For stranded datasets (ROSMAP and EADB Belgian) strand info is provided with "+" or "-" symbols, and if not stranded, "?" symbol is used.</li> <li>junc_distance - Genomic distance between sQTL variant and splice junction start</li> <li>ma_samples - Number of samples carrying the minor allele</li> <li>ma_count - Total count of minor alleles</li> <li>maf - Minor allele frequency</li> <li>pval_nominal - Nominal P-value of the association</li> <li>slope - Slope of the association with respect to alternative (ALT) allele indicated on column 14</li> <li>slope_se - Standard error of the slope</li> <li>pval_nominal_threshold - Nominal <em>P</em>-value significant threshold for thissJunction</li> <li>min_pval_nominal - Most significant <em>P</em>-value observed for this sJunction</li> <li>pval_beta - permutation <em>P</em>-value obtained via beta approximation and later used to calculate Storey q-values</li> <li>junction - sJunction in chr:start-end splice junction format</li> <li>cluster - The splice cluster of this sJunction</li> <li>genes - Genes overlapping with this sJunction (if any), based on GENCODEv24 (AMP-AD) and GENCODEv32 (EADB Belgian)</li> <li>GRCh38_chr_pos - Genomic position of the variant, separated by underscore</li> <li>ref_alt - Reference (REF) and alternative (ALT) allele of the variant, separated by ">" sign. ALT is the tested (A1) allele</li> </ol> <p>Of note, we also mapped the significant eQTLs in the same datasets (please see the data availability section of the manuscript or the GitHub repository).</p> <p><strong>Splicing TWAS reference panels</strong></p> <p>Custom splicing TWAS reference panels prepared using FUSION pipeline (<a href="http://gusevlab.org/projects/fusion/">http://gusevlab.org/projects/fusion/</a>) in AMP-AD brain and EADB Belgian LCL cohorts. All data in hg38/GRCh38 genome build. In each directory, you will find ".pos", ."profile", and ".profile.err" files. These are explained in the FUSION website as well, but briefly these are:</p> <ol> <li><strong>.pos:</strong> This is a position file that describes the 1Mb extended splice junction start and end coordinates for each calculated weight file for splice junction phenotype. Used for scanning the variants in those coordinates for TWAS.</li> <li><strong>.profile: </strong>This informs about all prediction weights calculated, in terms of number of variants in the model, heritability information, and R2 info for each prediction model used (top1, blup, enet, bslmm, lasso; bslmm was not used therefore has NA values).</li> <li><strong>.profile.err: </strong>This summarizes the reference panel in terms of average hsq (with SD), and which model is the best performing.</li> </ol> <p>Each TWAS weight is provided in a .RDat file under <strong>All_Splicing_Weights</strong>, and in this data we included all calculated functional weights independent of the fact that they are heritable features or not. In our TWAS analyses, we included the heritable functional weights at a hsq <em>P</em>-value ≤ 0.05 level.</p> <p>Please also see the expression TWAS reference panels we prepared in the same datasets (see the data availability section of the manuscript or the GitHub repository). If you need an LD reference data in hg38/GRCh38 genome build based on 1000 Genomes NFE samples (whose variant ID annotation are matching to these functional weights), suitable for running the TWAS/FUSION pipeline, please contact us.</p>
Influences of the 1855 AD Huanghe (Yellow River) Relocation on Sedimentary Organic Carbon Burial in the Southern Yellow Sea
<p>This is the original data used in the manuscript titled "Influences of the 1855 AD Huanghe (Yellow River) Relocation on Sedimentary Organic Carbon Burial in the Southern Yellow Sea" which has been accepted by Frontiers in Marine Science. The data is from a box-core HH12 recovered from the southern Yellow Sea (123.50°E, 35.00°N; core length: 48 cm; water depth: 77 m; time span: ~300 yr). This excel includes depth, year, TOC, TN, biomarkers and other proxy record.</p> <p>Full Article at: <a href="https://www.frontiersin.org/articles/10.3389/fmars.2022.824617/full">https://www.frontiersin.org/articles/10.3389/fmars.2022.824617/full</a></p>
Performance of different augmented writing tools on german job ads
<p>This dataset was created as part of an empirical analysis of four German-language augmented writing technologies for detecting gender exclusion. For this purpose, approximately 160,000 job postings from three different platforms were collected and evaluated using the technologies. The dataset primarily contains the number of expressions extracted per job posting, as well as the gender scores and categories calculated by the technologies. Together with variable descriptions and the list of keywords used to sample the leading positions, this dataset serves as additional information for a manuscript under review.</p>
Aedoeagus in ventral (a), dorsal (ad) and lateral (al) views: (2a, 2al) N. drescheri iacobi; (3a, 3al) N. germana; (4a, 4al) N. aeneipennis; (5a, 5al) N. limbifer; (6a, 6al) N. ignipennis; (7a, 7al) N. barbarossa; (8a, 8al) N. eleanorae; (10a, 10al) N. borneensis; (11a, 11al) N. brendelli; (12a, 12al) N. oxoniensis; (13ad, 13al) N. calcicola; (14a, 14al) N. argentifer; (15a. 15al) N. sarahae. Male sternite VII: (4c) N. aeneipennis in The genus Naddia in Borneo (Coleoptera: Staphylinidae: Staphylininae)
Aedoeagus in ventral (a), dorsal (ad) and lateral (al) views: (2a, 2al) N. drescheri iacobi; (3a, 3al) N. germana; (4a, 4al) N. aeneipennis; (5a, 5al) N. limbifer; (6a, 6al) N. ignipennis; (7a, 7al) N. barbarossa; (8a, 8al) N. eleanorae; (10a, 10al) N. borneensis; (11a, 11al) N. brendelli; (12a, 12al) N. oxoniensis; (13ad, 13al) N. calcicola; (14a, 14al) N. argentifer; (15a. 15al) N. sarahae. Male sternite VII: (4c) N. aeneipennis
Packet time delivery on ad hoc network
<p>This dataset represents the 90,000 simulations of package delivery on an ad hoc network represented by one victim, gateways and mobile nodes.<br> <br> There are 900 different scenarios. For each one, we ran 100 simulations.<br> <br> For more information about this dataset, see the paper: "<em>Framework for gateways specification in an ad hoc network for natural disaster situations</em>"<br> <br> 1st column: l - length of the side of the square area (in meters).<br> 2nd column: n - number of nodes.<br> 3rd column: g - number of gateways.<br> 4th column: p - number of position of victim (see the paper).<br> 5th column to 104th column: t0 to t99 - time of package delivery (for each of 100 simulations).<br> 105th column: m - mean.<br> 106th column: s - standard deviation.<br> 107th column: f - number of failures on package delivery (counted on the 100 simulations).</p>
Figure 9. Adding-Reducing mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Adding a small value from one weight and decrement that value to another weight. The chosen<br> weights should be involved in summation of 1.0. Figure 9 shows an example, we add 0.001 from<br> one weight and decrement the same value from another weight.</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.