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8,009 results for “GUIDs”
Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer
<p>The fourth dataset dedicated to the <a href="http://openguidedwaves.de/">Open Guided Waves</a> platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. </p> <p>A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.</p> <p><br> Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.</p> <p>The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.</p> <p>The detailed description of the dataset is published in Data in Brief Journal [3].</p>
Developmental change in prefrontal cortex recruitment supports the emergence of value-guided memory
Open the record for dataset details and reuse information.
Tab and comma delimited versions of Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila)
<p><span><em><strong>Introduction</strong></em></span></p> <p>This archive includes a tab-delimited (tsv) and comma-delimited (csv) version of the <a href="http://www.discoverlife.org/mp/20q?act=x_checklist&guide=Apoidea_species">Discover Life bee species guide and world checklist </a>(Hymenoptera: Apoidea: Anthophila). Discover Life is an important resource for bee species names and this update is from Draft-55, November 2020. Data were accessed and transformed into a tsv file in August 2023 using <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI) <a href="https://github.com/globalbioticinteractions/nomer">nomer</a> software. GloBI now incorporates the Discover Life bee species guide and world checklist in its functionality for searching for bee interactions.</p> <p><span><strong>Update! New Dataset also includes Subgenera Names</strong></span></p> <p>A new, tab-delimited version of the Discover Life taxonomy as derived from Dorey et. al, 2023 can be found via Zenodo at <a href="https://doi.org/10.5281/zenodo.10463762">https://doi.org/10.5281/zenodo.10463762</a>. This version of the Discover Life world species guide and checklist includes subgeneric names.</p> <p><span><strong>Citation</strong></span></p> <p><strong>Please cite the original source for this data as:</strong></p> <blockquote> <p><strong>Ascher, J. S. and J. Pickering. 2022.<br>Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila).<br>http://www.discoverlife.org/mp/20q?guide=Apoidea_species </strong>Draft-56, 21 August, 2022</p> </blockquote> <p><span><strong><em>nomer</em></strong></span></p> <p>nomer is a command-line application for working with taxonomic resources offline. nomer incorporates many of the present taxonomic catalogs (e.g., catalog of life, ITIS, EOL, NCBI) and provides simple tools for comparing between resources or resolving taxonomic names based on one or more taxonomic name catalogs. Discover Life is in nomer version 0.5.1 and this full dataset can be recreated by installing nomer from <a href="https://github.com/globalbioticinteractions/nomer">https://github.com/globalbioticinteractions/nomer</a> and running</p> <blockquote> <p>$ nomer list discoverlife > discoverlife.tsv</p> </blockquote> <p><span><em><strong>Data Columns</strong></em></span></p> <p>Discover Life provides a world name checklist and includes other names (synonyms and homonyms) that refer to the same species. In the tsv file, the provided name is both the accepted, or checklist name, or "other name." All names will be listed as a providedName. Below is an example subset of the transformed version of the data.</p> <ul> <li>providedExternalId= link to name on Discover Life</li> <li>providedName=an accepted or "<em>other name</em>" in the Discover Life bee checklist. "Other names" can be synonyms or homonyms.</li> <li>providedAuthorship=authorship for the providedName</li> <li>providedRank=rank of the providedName</li> <li>providedPath=higher taxonomy of the providedName. This will be the same as the accepted name or resolvedName</li> <li>relationName=relationship between the "<em>other name</em>" and the bee name in the Discover Life checklist. It may include itself</li> <li>resolvedExternalID=an <strong>accepted name</strong> in the Discover Life bee checklist</li> <li>resolvedExternalId=link to name on Discover Life</li> <li>resolvedAuthorship=authorship of the accepted, or checklist name</li> <li>resolvedRank=rank of the accepted, or checklist name</li> <li>resolvedPath=higher taxonomy of the accepted, or checklist name</li> </ul> <p><span><em><strong>Changes</strong></em></span></p> <p>No major changes to format in this version.</p> <p><span><em><strong>References</strong></em></span></p> <p>Jorrit Poelen, & José Augusto Salim. (2022). globalbioticinteractions/nomer: (0.2.11). Zenodo. https://doi.org/10.5281/zenodo.6128011</p> <p>Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Dorey, J.B., Fischer, E.E., Chesshire, P.R. et al. A globally synthesised and flagged bee occurrence dataset and cleaning workflow. Sci Data 10, 747 (2023). https://doi.org/10.1038/s41597-023-02626-w</p>
Atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light
<p>This repository contains the data used in an experiment that demonstrates atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light. For more information, see the following publication:</p> <ul> <li><a href="https://doi.org/10.1038/s41566-022-00987-z">10.1038/s41566-022-00987-z</a></li> <li><a href="https://doi.org/10.48550/arXiv.2107.07272">10.48550/arXiv.2107.07272</a></li> </ul> <p>We provide the data in text files encoded in the Unicode standard UTF-8. In the following, we describe the files in more detail.</p> <p>The measured evolution of the signal transmission presented in Fig. 2<strong>b</strong> is provided in the file “source_data_fig2b.txt”. The file has five columns that are separated by the delimiter “, ”:</p> <ul> <li>the time in microseconds,</li> <li>the signal transmission in the 1→2 direction,</li> <li>the error of the signal transmission in the 1→2 direction,</li> <li>the signal transmission in the 1→2 direction,</li> <li>and the error of the signal transmission in the 1→2 direction.</li> </ul> <p>We provide the theory data in the additional file “theory_fig2b.txt”. It contains three columns that are separated by the delimiter “, ” :</p> <ul> <li>the time in microseconds,</li> <li>the calculated signal transmission in the 1→2 direction,</li> <li>the calculated signal transmission in the 2→1 direction.</li> </ul> <p>In the files “source_data_fig2c.txt”, “source_data_fig2d.txt”, and “source_data_fig3b.txt”, we provide the data of the bar plots in Fig. 2<strong>c</strong>, 2<strong>d</strong>, and 3<strong>b</strong>, respectively. In every file, the first column indicates the measurement direction. The following columns contain the detected mean signal transmission with the corresponding errors for various initial atomic spin states defined by the magnetic quantum number <em>m<sub>F</sub></em>.</p>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
A pangenome-guided manually curated library of transposable elements for Zymoseptoria tritici
<p>A manually-curated TE consensus library generated using a panel of 19 reference genomes for <em>Zymoseptoria tritici</em><sup>1-3</sup> along with reference genome assemblies for the sister species <em>Z. ardabiliae</em>, <em>Z. brevis</em>, <em>Z. pseudotritici</em>, and <em>Z. passerinii<sup>4</sup></em>. </p> <p> </p> <p><strong>Methods</strong></p> <p>Putative TE consensus sequences were first obtained by annotating all 23 genome assemblies<sup>1–4</sup> with Earl Grey with default settings (v3.0; <a href="https://github.com/TobyBaril/EarlGrey">https://github.com/TobyBaril/EarlGrey</a>)<sup>5,6</sup>. Consensus sequences generated from each reference genome were clustered using CD-Hit-Est (v4.8.1)<sup>7,8</sup> to group sequences with 90% similarity across 80% of the longer sequence length (<em>-n 8 -d 0 -aL 0.8 -c 0.90 -G 0 -g 1 -b 500 -r 1</em>) to reduce redundancy whilst preventing the collapsing of chimeric sequences. Consensus sequences <100bp were removed, as these are unlikely to represent true TE sequences. Each consensus sequence was then subject to manual curation as described by Goubert et al. (2022)<sup>9</sup>. Briefly, genomic copies of each TE were obtained using a “BLAST, Extract, Extend” process to recover genomic copies from each of the 23 reference genome assemblies with 1,000 flanking bases at either end<sup>9,10</sup>. For families with >100 BLASTN hits, the 25 longest hits were selected, along with 75 random hits. Multiple alignments were generated for each putative TE family using MAFFT (v7.505) with the --auto flag<sup>11</sup>. Columns composed of >=80% gaps were removed with T-COFFEE (v13.45.0.4846264)<sup>12</sup>. Subsequently, all sequence alignments were manually curated to define TE boundaries and remove regions of low conservation and rare insertions. Following manual curation, new majority-rule consensus sequences were generated with EMBOSS (v6.6.0.0) cons<sup>13</sup>. TE-Aid (<a href="https://github.com/clemgoub/TE-Aid/">https://github.com/clemgoub/TE-Aid/</a>) was used to aid visual inspection and to identify diagnostic features for classification of extended consensus sequences. Following this, TIRs were recorded if present, and nhmmscan (HMMER v3.3.2)<sup>14</sup> was used to identify homology to known curated elements in Dfam (v3.7). Combining this information, each TE consensus sequence was manually classified using available information following the naming convention ‘>ZymTri_2023_family_[n]#[Classification]/[Family]’. Consensus sequences classified with low confidence have a ‘?’ added to the name, as well as the string ‘_LowConf’. To reduce redundancy in the final TE library, sequences were clustered to the family-level using the 80-80-80 rule implemented in CD-hit-est<sup>9,15 </sup>(<em>-d 0 -aS 0.8 -c 0.8 -G 0 -g 1 -b 500 -r 1</em>). The representative sequence for each cluster was manually selected to select the sequence with the highest classification confidence, also defined as the ‘most intact consensus’. Chimeric sequences erroneously clustered were manually separated to retain sequences for the chimeric TE and the individual elements that generated the chimer.</p> <p> </p> <p><strong>References</strong></p> <p>1. Badet, T., Oggenfuss, U., Abraham, L., McDonald, B. A. & Croll, D. A 19-isolate reference-quality global pangenome for the fungal wheat pathogen Zymoseptoria tritici. <em>BMC Biol.</em> <strong>18</strong>, 12 (2020).</p> <p>2. Goodwin, S. B. <em>et al.</em> Finished genome of the fungal wheat pathogen Mycosphaerella graminicola reveals dispensome structure, chromosome plasticity, and stealth pathogenesis. <em>PLoS Genet.</em> <strong>7</strong>, e1002070 (2011).</p> <p>3. Plissonneau, C., Hartmann, F. E. & Croll, D. Pangenome analyses of the wheat pathogen Zymoseptoria tritici reveal the structural basis of a highly plastic eukaryotic genome. <em>BMC Biol.</em> <strong>16</strong>, 5 (2018).</p> <p>4. Feurtey, A. <em>et al.</em> Genome compartmentalization predates species divergence in the plant pathogen genus Zymoseptoria. <em>BMC Genomics</em> <strong>21</strong>, 588 (2020).</p> <p>5. Baril, T., Imrie, R. M. & Hayward, A. Earl Grey: a fully automated user-friendly transposable element annotation and analysis pipeline. (2022) doi:10.21203/rs.3.rs-1812599/v1.</p> <p>6. Baril, T., Galbraith, J. & Hayward, A. <em>Earl Grey</em>. (Zenodo, 2023). doi:10.5281/ZENODO.8116025.</p> <p>7. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. <em>Bioinformatics</em> <strong>22</strong>, 1658–1659 (2006).</p> <p>8. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. <em>Bioinformatics</em> <strong>28</strong>, 3150–3152 (2012).</p> <p>9. Goubert, C. <em>et al.</em> A beginner’s guide to manual curation of transposable elements. <em>Mob. DNA</em> <strong>13</strong>, 7 (2022).</p> <p>10. Camacho, C. <em>et al.</em> BLAST+: Architecture and applications. <em>BMC Bioinformatics</em> <strong>10</strong>, 1–9 (2009).</p> <p>11. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. <em>Mol. Biol. Evol.</em> <strong>30</strong>, 772–780 (2013).</p> <p>12. Notredame, C., Higgins, D. G. & Heringa, J. T-coffee: a novel method for fast and accurate multiple sequence alignment. <em>J. Mol. Biol.</em> <strong>302</strong>, 205–217 (2000).</p> <p>13. Rice, P., Longden, L. & Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. <em>Trends Genet.</em> <strong>16</strong>, 276–277 (2000).</p> <p>14. Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. <em>Bioinformatics</em> <strong>29</strong>, 2487–2489 (2013).</p> <p>15. Wicker, T. <em>et al.</em> A unified classification system for eukaryotic transposable elements. <em>Nat. Rev. Genet.</em> <strong>8</strong>, 973–982 (2007).</p>
Evolution-Guided Engineering of Trans-Acyltransferase Polyketide Synthases
<p>Data underlying the manuscript 'Evolution-Guided Engineering of <em>Trans</em>-Acyltransferase Polyketide Synthases' by Mabesoone, Leopold-Messer and Minas et al.</p> <p>The repository contains:</p> <p>Sequencing data - Genbanks files of construct designs, ab1 and fasta files for Sanger sequencing. For whole plasmid sequencing, fasta, annotated GenBank files, overviews of sequencing statistics and fastq files for selected plasmids, for which fastq files were provided by the sequencing service, are provided.</p> <p>NMR data - raw data and MestReNova files. Also includes HPLC-MS traces of isolated compounds.</p> <p>HPLC-MS data - Raw data collected on Thermo-Fisher instruments. This data can be analyzed with the Xcalibur software suite. The mzXML data can be analyzed with the Python scripts provided in the scripts folder to generate the images shown in the SI. The data collected for Bacillus and Serratia is MS1 data. The data collected for Gynuella also contains MS-MS data.</p> <p>SCA data - Python scripts, GenBank files and produced data underlying the SCA.</p> <p>Bioactivity data - Data underlying the toxicity assays in Figure S122.</p> <p> </p>
RNA-Protein Interaction Prediction Using Network-Guided Deep Learning
<p>RNA-protein interactions are critical to various life processes, including fundamental translation and gene regulation. Identifying these interactions is vital for understanding the mechanisms underlying life processes. Then, ZHMolGraph is an advanced pipeline that integrates graph neural network sampling strategy and unsupervised large language models to enhance binding predictions for novel RNAs and proteins.</p> <div> </div>
BIM Guides Survey
<p>We present the result of a survey of BIM Guides available online, in Brazil or internationally, based on a reference list presented in Sacks, Gurevich, and Shrestha (2016) (<a href="https://www.itcon.org/paper/2016/29">https://www.itcon.org/paper/2016/29</a>) and expanded with Internet searches to identify more guides on Building Information Modeling. The synthesis or title of the guides allows the teacher, or anyone interested in general, to identify material to support the process of implementing BIM in a project.</p> <p>The guides are characterized by the following fields:</p> <ol> <li> <p>TITLE: title of the document or site;</p> </li> <li> <p>COUNTRY: country of origin of the guide;</p> </li> <li> <p>REFERENCE: bibliographic reference of the guide in ABNT format;</p> </li> <li> <p>LINK: URL to the online location of the guide;</p> </li> <li> <p>SUMMARY: summary of the content.</p> </li> </ol>
The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities
<p>The Bomber's Baedeker</p> <p>The two-volume printed work “The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities” was produced during the Second World War by the British Foreign Office and the Ministry of Economic Warfare. It lists towns and cities of the German Reich with more than a thousand inhabitants and information on their war-related infrastructure, industrial and production facilities. Only four verified copies still exist worldwide and none of them have been accessible for scholarly digital use until now. “The Bomber's Baedeker” was re-discovered in 2019 in the library of the Leibniz Institute of European History (IEG), digitised in cooperation with the Mainz University Library and made accessible and processed by the Digital Historical Research | DH Lab and the Darmstadt University of Applied Sciences as part of a cross-institutional cooperation (including courses with students) so that “The Bomber's Baedeker” can now be used, analysed and processed as an open, machine-readable data source in compliance with FAIR principles.</p>
Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
<p>Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.</p>
Simulations from "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations"
<p>This dataset contains the simulation results from the article "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations" (submitted to MNRAS).</p> <p><br> The data is grouped by simulation and by particle type (gas, sinks and stars). Gas is uploaded with one snapshot per 0.05 Myr, sinks and stars with one snapshot per 0.01 Myr. The data is stored in AMUSE data format, which uses hdf5.</p>
Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change
<p>This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).</p>
Visualization of guided elastic waves generated by SHPFP transducers
<p>DualSH-PFP_measurement.avi: velocity magnitude generated by the Dual SHPFP and measured by Laser Doppler vibrometry</p> <p>SH-PFP_measurement.avi: velocity magnitude generated by the original SHPFP and measured by Laser Doppler vibrometry</p> <p>DualSHPFP_simulation.avi: circumferential component of the velocity generated by the Dual SHPFP and determined by simulation</p> <p>SHPFP_simulation.avi: circumferential component of the velocity generated by the original SHPFP and determined by simulation</p> <p>All simulations and measurements are performed at a center frequency of 80 kHz.</p>
Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
Natural Language-Guided Programming User Study
<p>In this dataset you find the user study data that was used in the <strong><em>Natural Language-Guided Programming</em></strong> paper, which is accepted for Onward! 2021. A preprint can be found here <a href="https://arxiv.org/pdf/2108.05198.pdf">https://arxiv.org/pdf/2108.05198.pdf</a>. The dataset consists of the following files:</p> <ul> <li> <p>benchmark.json contains 201 test cases. Each test case consists of context, a natural language intent and target code. The test cases are intended to evaluate a model that can predict code giving a piece of context code and a natural language intent. The test cases were derived from Jupyter notebooks that were crawled from Github projects with permissive licenses. In the project_metadata field you find information about the original project such as its git url and license.</p> </li> <li> <p>predictions-annotated.json contains predictions of the three models used in the paper for 100 test cases in benchmark.json. Each prediction is accompanied with qualitive assesments from three annotators.</p> </li> <li> <p>train-index.jsonl is the list of github projects that were used for training the models.</p> </li> <li> <p>eval-index.jsonl is a list of github projects that we kept separate for evaluation. The benchmark.json was created from a random subset of the projects in this list.</p> </li> </ul> <p>For more details we refer to the paper.</p>
Fighting COVID-19 with computational tools: an AI guided review of 17,000 studies - The CSCoV database.
<p>CSCoV (Computational Studies about COVID-19) is a dataset containing COVID-19 related studies extracted from PubMed, bioRxiv, medRxiv, and arXiv, together with article and author related metrics obtained from Semantic Scholar (plus page views from bioRxiv and medRxiv). Using machine learning, the articles are categorized in six topics (Pharmacology, Genomics, Epidemiology, Healthcare, Clinical Medicine, Clinical Imaging) and prioritized. The database is periodically updated.</p> <ul> <li>Publication: TBA</li> <li>Files included in this release: <ul> <li>cscov_09_2021.png: dataset statistics for the current CSCoV release.</li> <li>cscov_09_2021.tsv: CSCoV database.</li> <li>schema.json: metadata.</li> <li>cscov_09_2021.tar.gz: Doc2Vec and DeepWalk features used for the DL model</li> </ul> </li> <li> <p>Source code: <a href="https://github.com/SFB-KAUST/covid-review">https://github.com/SFB-KAUST/covid-review</a></p> </li> </ul>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</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.