Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
30,131
datasets available to search
ShareScore release 0.9.0
Dataset results
30,131 results for “control”
Liquid Flow and Control Without Solid Walls
<p>This repository contains additional data related to the publication: 10.26434/chemrxiv.7207001</p> <p>Contained in python_magneto_fluidics.zip are all the files needed to calculate magnetic fields of any assembly of cuboid permanent magnets such as used in this paper, along with the equilibrium diameters for each antitube-ferrofluid combination.</p> <p>data figures.zip contains all the experimental data plotted in the figures, consisting of data in figures:</p> <p>Main Text: 2, 3, 4<br> Extended Data: E2, E3, E4, E6, E8</p>
Table of Indications and Regimens from the National Cancer Control Programme, Ireland
<h4>Description:</h4> <p>A table containing all indications published by the National Cancer Control Programme (NCCP), Ireland. Each entry has an indication code, description, and disease; regimen code, name, and URL; and information regarding whether the indication contains molecular diagnostic criteria. Entries were last updated from the NCCP website on 2025-May-27.</p> <h4>Headings:</h4> <ul> <li>IndicationCode: NCCP indication code (ex: 00537a).</li> <li>IndicationDesc: Description of the indication taken from its relevant regimen (ex: "Monotherapy for the treatment of adults with relapsed or refractory CD22-positive B cell precursor acute lymphoblastic leukaemia (ALL). Adult patients with Philadelphia chromosome positive (Ph+) relapsed or refractory B cell precursor ALL should have failed treatment with at least 1 tyrosine kinase inhibitor (TKI).")</li> <li>CancerType: Manual annotation of disease category for the indication (ex: Leukaemia).</li> <li>HasGeneticCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of genetic criteria for the indication, as described in the indication description or regimen document.</li> <li>GeneticCriteria: If HasGeneticCriteria is TRUE, the relevant criteria listed (ex: BCR-ABL1 positive).</li> <li>HasBiomarkerCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of cellular biomarker criteria for the indication, as described in the indication description or regimen document.</li> <li>BiomarkerCriteria: If HasBiomarkerCriteria is TRUE, the relevant criteria listed (ex: CD22+).</li> <li>HasMolecularCriteria: For convenience, column stating TRUE if HasBiomarkerCriteria is True or HasGeneticCriteria is True.</li> <li>RegimenCode: NCCP code for the regimen associated with the indication (ex: 537).</li> <li>RegimenName: NCCP regimen name (ex: Inotuzumab ozogamicin Monotherapy)</li> <li>NCCPRegimenCategories: NCCP disease categories associated with the regimen (ex: Leukaemia/BMT).</li> <li>RegimenURL: URL to the NCCP regimen document.</li> <li>Notes: Miscellaneous notes containing notes from NCCP regimen documents or further explanations.</li> </ul> <p><br> </p>
Data for: "Direct photochemical control of imine exchange reactions"
<div>This dataset is all of the data produced which relates to the text "Direct photochemical control of imine exchange reactions"</div> <div> </div> <div>The data set is separated loosely into </div> <div> </div> <div>1) Computational-data : All of the simulated data<br> <br>2) Kinetics : All of the data which lead to the nmr-time monitored experiments, where samples were equilibrated then irradiated and heated<br> <br>3) Photophysical-characterisation : All UV-VIS spectra and luminance spectra<br> <br>4) Synthetic-data-and-charcterisation : the details of the synthetises, and the 1H NMR, 13C NMR, IR, Mass-Spec, and Elemental analysis data<br> <br> <br>Generally within these folders, subfolders, subsubfolders etc. the folders contain zipped HTML copies of the lab notebooks, images of the graphs which result from them, and code which has generated them. Within further folders will be data which produces these graphs.<br> <br> <br>The way to interact with the compressed HTML lab notebooks, is to unzip them, and then open the HTML files.<br> <br> <br>Warning: The code for generating the graphs has not been cleaned up; it is presented as it was at time of publication. It will take some time for you to follow it, not because it is complex, but because it includes a lot of unnecessary diversions. Often I was working out how to process the data as I programmed them.<br> </div> <div> </div> <div> </div> <div>Where to find this data for each figure is given as follows:</div> <div> </div> <div>Figure 1: -not data-</div> <div> </div> <div>Figure 2: .\photophysical-characterisation\Imines-UV-VIS</div> <div> </div> <div>Figure 3: .\Kinetics\Kinetic-main</div> <div> </div> <div>Figure 4: .\Kinetics\Kinetic-temperature</div> <div> </div> <div>Figure 5: .\Computational-data</div> <div> </div> <div>Figure S1: .\photophysical-characterisation\Amines-UV-VIS</div> <div> </div> <div>Figure S2: .\Kinetics\Supplementary-kinetic</div> <div> </div> <div>Figure S3: .\Kinetics\Supplementary-temperature-kinetic</div> <div> </div> <div>Figure S4: .\Computational-data</div> <div> </div> <div>Figure S5: .\Kinetics\Kinetic-main\nmr\A-4-20-1mnova.mnova</div> <div> </div> <div>Figure S6: .\Kinetics\Kinetic-main\nmr\A-4-21-1mnova.mnova</div> <div> </div> <div>Figure S7: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div> </div> <div>Figure S8: .\Synthetic-data-and-characterisation\[compound-data]\1H-NMR</div> <div> </div> <div>Figure S9: .\photophysical-characterisation\LED-Luminence</div> <div> </div> <div>Figure S10: .\photophysical-characterisation\LED-Luminence</div> <div> </div> <div>Figure S11: -not data-</div> <div> </div> <div>Figure S12: -not data-</div> <div> </div> <div>Figure S13: .\Synthetic-data-and-characterisation\Characterisation_A-imine\1H-NMR</div> <div> </div> <div>Figure S14: .\Synthetic-data-and-characterisation\Characterisation_MA-imine\1H-NMR</div> <div> </div> <div>Figure S15: .\Synthetic-data-and-characterisation\Characterisation_DMMA-imine\1H-NMR</div> <div> </div> <div>Figure S16: .\Synthetic-data-and-characterisation\Characterisation_FLUR-imine\1H-NMR</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>The dataset contains details of the following compounds:</div> <div> </div> <div>Article name: A-Imine</div> <div> </div> <div>IUPAC name: (E)-N-phenyl-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: C1(/N=C/C2=CC(SC=C3)=C3S2)=CC=CC=C1</div> <div> </div> <div>SLN: C[2](N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)=CC=CC=C@3</div> <div> </div> <div>InChI: 1S/C13H9NS2/c1-2-4-10(5-3-1)14-9-11-8-13-12(16-11)6-7-15-13/h1-9H/b14-9+</div> <div> </div> <div>InChI key: FMENSGUUZYOJTA-NTEUORMPSA-N</div> <div> </div> <div> </div> <div>Article name: DMMA-Imine</div> <div> </div> <div>IUPAC name: (E)-N,N-dimethyl-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)aniline</div> <div> </div> <div>SMILES Code: CN(C)C1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: CN(C)C[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div> </div> <div>InChI: InChI=1S/C15H14N2S2/c1-17(2)12-5-3-11(4-6-12)16-10-13-9-15-14(19-13)7-8-18-15/h3-10H,1-2H3/b16-10+</div> <div> </div> <div>InChI key: XWBXQJOJYSCJCK-MHWRWJLKSA-N</div> <div> </div> <div> </div> <div>Article name: FLUR-Imine</div> <div> </div> <div>IUPAC name: (E)-N-(9H-fluoren-2-yl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: C1(C=CC=C2)=C2C(C=CC(/N=C/C3=CC(SC=C4)=C4S3)=C5)=C5C1</div> <div> </div> <div>SLN: C[1](C=CC=C[13])=C@13C(C=CC(N=[S=I]CC[16]=CC(SC=C[23])=C@23S@16)=C[8])=C@9C@2</div> <div> </div> <div>InChI: InChI=1S/C20H13NS2/c1-2-4-17-13(3-1)9-14-10-15(5-6-18(14)17)21-12-16-11-20-19(23-16)7-8-22-20/h1-8,10-12H,9H2/b21-12+</div> <div> </div> <div>InChI key: RSZKSUGPHCONDB-CIAFOILYSA-N</div> <div> </div> <div> </div> <div>Article name: MA-Imine</div> <div> </div> <div>IUPAC name: (E)-1-(thieno[3,2-b]thiophen-2-yl)-N-(p-tolyl)methanimine</div> <div> </div> <div>SMILES Code: CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: CC[1]=CC=C(N=[S=I]CC[9]=CC(SC=C[17])=C@17S@10)C=C@2</div> <div> </div> <div>InChI: InChI=1S/C14H11NS2/c1-10-2-4-11(5-3-10)15-9-12-8-14-13(17-12)6-7-16-14/h2-9H,1H3/b15-9+</div> <div> </div> <div>InChI key: XARWNOWDJRWLJY-OQLLNIDSSA-N</div> <div> </div> <div> </div> <div>IUPAC name: (E)-4-((thieno[3,2-b]thiophen-2-ylmethylene)amino)benzonitrile</div> <div> </div> <div>SMILES Code: N#CC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: N#CC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div> </div> <div>InChI: InChI=1S/C14H8N2S2/c15-8-10-1-3-11(4-2-10)16-9-12-7-14-13(18-12)5-6-17-14/h1-7,9H/b16-9+</div> <div> </div> <div>InChI key: MTCNXZQWYYVDCG-CXUHLZMHSA-N</div> <div> </div> <div> </div> <div>IUPAC name: (E)-N-(4-methoxyphenyl)-1-(thieno[3,2-b]thiophen-2-yl)methanimine</div> <div> </div> <div>SMILES Code: COC1=CC=C(/N=C/C2=CC(SC=C3)=C3S2)C=C1</div> <div> </div> <div>SLN: COC[5]=CC=C(N=[S=I]CC[8]=CC(SC=C[16])=C@16S@9)C=C@6</div> <div> </div> <div>InChI: InChI=1S/C14H11NOS2/c1-16-11-4-2-10(3-5-11)15-9-12-8-14-13(18-12)6-7-17-14/h2-9H,1H3/b15-9+</div> <div> </div> <div>InChI key: WIBJKKCQZPFCIZ-OQLLNIDSSA-N</div> <div> </div> <div> </div> <div>Article name: A-Amine</div> <div> </div> <div>IUPAC name: Benzenamine</div> <div> </div> <div>Common name: Aniline</div> <div> </div> <div>SMILES Code: NC1=CC=CC=C1</div> <div> </div> <div>SLN: NC[2]=CC=CC=C@3</div> <div> </div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div> </div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 62-53-3</div> <div> </div> <div> </div> <div> </div> <div>Article name: MA-Amine</div> <div> </div> <div>IUPAC name: 4-Aminotoluene</div> <div> </div> <div>Common name: p-toludine</div> <div> </div> <div>SMILES Code: NC1=CC=CC=C1</div> <div> </div> <div>SLN: NC[2]=CC=CC=C@3</div> <div> </div> <div>InChI: InChI=1S/C6H7N/c7-6-4-2-1-3-5-6/h1-5H,7H2</div> <div> </div> <div>InChI key: PAYRUJLWNCNPSJ-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 106-49-0</div> <div> </div> <div> </div> <div>Article name: DMMA-Amine</div> <div> </div> <div>IUPAC name: N1,N1-dimethylbenzene-1,4-diamine</div> <div> </div> <div>SMILES Code: NC1=CC=C(N(C)C)C=C1</div> <div> </div> <div>SLN: NC[2]=CC=C(N(C)C)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C8H12N2/c1-10(2)8-5-3-7(9)4-6-8/h3-6H,9H2,1-2H3</div> <div> </div> <div>InChI key: BZORFPDSXLZWJF-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 99-98-9</div> <div> </div> <div> </div> <div>Article name: FLUR-Amine</div> <div> </div> <div>IUPAC name: 9H-fluoren-2-amine</div> <div> </div> <div>SMILES Code: NC1=CC(CC2=C3C=CC=C2)=C3C=C1</div> <div> </div> <div>SLN: NC[2]=CC(CC[8]=C[9]C=CC=C@9)=C(@10)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C13H11N/c14-11-5-6-13-10(8-11)7-9-3-1-2-4-12(9)13/h1-6,8H,7,14H2</div> <div> </div> <div>InChI key: CFRFHWQYWJMEJN-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 153-78-6</div> <div> </div> <div> </div> <div>IUPAC name: 4-aminobenzonitrile</div> <div> </div> <div>SMILES Code: NC1=CC=C(C#N)C=C1</div> <div> </div> <div>SLN: NC[2]=CC=C(C#N)C=C@3</div> <div> </div> <div>InChI: InChI=1S/C7H6N2/c8-5-6-1-3-7(9)4-2-6/h1-4H,9H2</div> <div> </div> <div>InChI key: YBAZINRZQSAIAY-UHFFFAOYSA-N</div> <div> </div> <div>CAS no: 873-74-5</div> <div> </div>
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
MarFERReT: an open-source, version-controlled reference library of marine microbial eukaryote functional genes
<p>Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of only about half of eukaryotic environmental transcripts. Here, we introduce version 1.0 of the Marine Functional EukaRyotic Reference Taxa (MarFERReT), an updated marine microbial eukaryotic sequence library with a version-controlled framework designed for taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 marine eukaryote genomes and transcriptomes from multiple sources and assessed these candidate entries for sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in the library. MarFERReT v1 contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. An accompanying MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.<br><br>MarFERReT is linked to a code repository hosting containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT here: <a href="https://github.com/armbrustlab/marferret">https://github.com/armbrustlab/marferret</a></p> <p>The raw source data for the 902 candidate entries considered for MarFERReT v1.1.1, including the 800 accepted entries, are available for download from their respective online locations. The source URL for each of the entries is listed here in MarFERReT.v1.1.1.entry_curation.csv, and detailed instructions and code for downloading the raw sequence data from source are available in the MarFERReT code repository (<a href="https://github.com/armbrustlab/marferret/blob/main/docs/process_clean_marmicrodb.log.sh">link</a>). </p> <p>This repository release contains MarFERReT database files from the v1.1.1 MarFERReT release using the following MarFERReT library build scripts: <strong>assemble_marferret.sh</strong>, <strong>pfam_annotate.sh</strong>, and <strong>build_diamond_db.sh</strong><br><br>The following MarFERReT data products are available in this repository:</p> <p><strong>MarFERReT.v1.1.1.metadata.csv</strong><br>This CSV file contains descriptors of each of the 902 database entries, including data source, taxonomy, and sequence descriptors. Data fields are as follows:</p> <ol> <li><strong>entry_id</strong>: Unique MarFERReT sequence entry identifier.</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y/N). The Y/N values can be adjusted to customize the final build output according to user-specific needs.</li> <li><strong>marferret_name</strong>: A human and machine friendly string derived from the NCBI Taxonomy organism name; maintaining strain-level designation wherever possible.</li> <li><strong>tax_id</strong>: The NCBI Taxonomy ID (taxID).</li> <li><strong>pr2_accession</strong>: Best-matching PR2 accession ID associated with entry</li> <li><strong>pr2_rank</strong>: The lowest shared rank between the entry and the pr2_accession</li> <li><strong>pr2_taxonomy</strong>: PR2 Taxonomy classification scheme of the pr2_accession</li> <li><strong>data_type</strong>: Type of sequence data; transcriptome shotgun assemblies (TSA), gene models from assembled genomes (genome), and single-cell amplified genomes (SAG) or transcriptomes (SAT).</li> <li><strong>data_source</strong>: Online location of sequence data; the Zenodo data repository (<a href="../">Zenodo</a>), the datadryad.org repository (<a href="http://datadryad.org/">datadryad.org</a>), MMETSP re-assemblies on Zenodo (MMETSP)17, NCBI GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">NCBI</a>), JGI Phycocosm (<a href="https://phycocosm.jgi.doe.gov/phycocosm/home">JGI-Phycocosm</a>), the TARA Oceans portal on Genoscope (<a href="http://www.genoscope.cns.fr/tara/">TARA</a>), or entries from the Roscoff Culture Collection through the METdb database repository (<a href="https://metdb.sb-roscoff.fr/metdb/">METdb</a>).</li> <li><strong>source_link</strong>: URL where the original sequence data and/or metadata was collected.</li> <li><strong>pub_year</strong>: Year of data release or publication of linked reference.</li> <li><strong>ref_link</strong>: Pubmed URL directs to the published reference for entry, if available.</li> <li><strong>ref_doi</strong>: DOI of entry data from source, if available.</li> <li><strong>source_filename</strong>: Name of the original sequence file name from the data source.</li> <li><strong>seq_type</strong>: Entry sequence data retrieved in nucleotide (nt) or amino acid (aa) alphabets.</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file.</li> <li><strong>source_name:</strong> Full organism name from entry source</li> <li><strong>original_taxID</strong>: Original NCBI taxID from entry data source metadata, if available</li> <li><strong>alias:</strong> Additional identifiers for the entry, if available</li> </ol> <p><br><strong>MarFERReT.v1.1.1.curation.csv</strong><br>This CSV file contains curation and quality-control information on the 902 candidate entries considered for incorporation into MarFERReT v1, including curated NCBI Taxonomy IDs and entry validation statistics. Data fields are as follows:</p> <ol> <li><strong>entry_id:</strong> Unique MarFERReT sequence entry identifier</li> <li><strong>marferret_name: </strong>Organism name in human and machine friendly format, including additional NCBI taxonomy strain identifiers if available.</li> <li><strong>tax_id</strong>: Verified NCBI taxID used in MarFERReT</li> <li><strong>taxID_status</strong>: Status of the final NCBI taxID (Assigned, Updated, or Unchanged)</li> <li><strong>taxID_notes</strong>: Notes on the original_taxID</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file</li> <li><strong>n_pfams</strong>: Number of Pfam domains identified in protein sequences</li> <li><strong>qc_flag</strong>: Early validation quality control flags for the following: LOW_SEQS; less than 1,200 raw sequences; LOW_PFAMS; less than 500 Pfam domain annotations.</li> <li><strong>flag_Lasek</strong>: Flag notes from Lasek-Nesselquist and Johnson (2019); contains the flag 'FLAG_LASEK' indicating ciliate samples reported as contaminated in this study.</li> <li><strong>VV_contam_pct</strong>: Estimated contamination reported for MMETSP entries in Van Vlierberghe et al., (2021).</li> <li><strong>flag_VanVlierberghe: </strong>Flag for a high level of estimated contamination, from 'flag_VanVlierberghe' values over 50%: FLAG_VV.</li> <li><strong>rp63_npfams</strong>: Number of ribosomal protein Pfam domains out of 63 total.</li> <li><strong>rp63_contam_pct</strong>: Percent of total ribosomal protein sequences with an inferred taxonomic identity in any lineage other than the recorded identity, as described in the Technical Validation section from analysis of 63 Pfam ribosomal protein domains.</li> <li><strong>flag_rp63</strong>: Flag for a high level of estimated contamination, from 'rp63_contam_pct' values over 50%: FLAG_RP63.</li> <li><strong>flag_sum: </strong>Count of the number of flag columns (`qc_flag`, `flag_Lasek`, `flag_VanVlierberghe`, and `flag_rp63`). All entries with one or more flag are nominally rejected ('accepted' = N); entries without any flags are validated and accepted ('accepted' = Y).</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y or N).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins.faa.gz</strong><br>This Gzip-compressed FASTA file contains the 27,951,013 final translated and clustered protein sequences for all 800 accepted MarFERReT entries. The sequence defline contains the unique identifier for the sequence and its reference (mftX, where 'X' is a ten-digit integer value). </p> <p> </p> <p><strong>MarFERReT.v1.1.1.taxonomies.tab.gz</strong><br>This Gzip-compressed tab-separated file is formatted for interoperability with the DIAMOND protein alignment tool commonly used for downstream analyses and contains some columns without any data. Each row contains an entry for one of the MarFERReT protein sequences in MarFERReT.v1.proteins.faa.gz. Note that 'accession.version' and 'taxid' are populated columns while 'accession' and 'gi' have NA values; the latter columns are required for back-compatibility as input for the DIAMOND alignment software and LCA analysis. </p> <p>The columns in this file contain the following information:</p> <ol> <li><strong>accession</strong>: (NA)</li> <li><strong>accession.version</strong>: The unique MarFERReT sequence identifier ('mftX').</li> <li><strong>taxid</strong>: The NCBI Taxonomy ID associated with this reference sequence.</li> <li><strong>gi</strong>: (NA).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins_info.tab.gz</strong><br>This Gzip-compressed tab-separated file contains a row for each final MarFERReT protein sequence with the following columns:</p> <ol> <li><strong>aa_id</strong>: the unique identifier for each MarFERReT protein sequence.</li> <li><strong>entry_id</strong>: The unique numeric identifier for each MarFERReT entry.</li> <li><strong>source_defline</strong>: The original, unformatted sequence identifier</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.best_pfam_annotations.csv.gz<br></strong>This Gzip-compressed CSV file contains the best-scoring Pfam annotation for intra-species clustered protein sequences from the 800 validated MarFERReT entries; derived from the hmmsearch annotations against Pfam 34.0 functional domains. This file contains the following fields:</p> <ol> <li><strong>aa_id</strong>: The unique MarFERReT protein sequence ID ('mftX').</li> <li><strong>pfam_name</strong>: The shorthand Pfam protein family name.</li> <li><strong>pfam_id</strong>: The Pfam identifier.</li> <li><strong>pfam_eval</strong>: hmm profile match e-value score</li> <li><strong>pfam_score:</strong> hmm profile match bitscore</li> </ol> <p><br><strong>MarFERReT.v1.1.1.dmnd</strong><br>This binary file is the indexed database of the MarFERReT protein library with embedded NCBI taxonomic information generated by the DIAMOND makedb tool using the build_diamond_db.sh script from the MarFERReT /scripts/ library. This can be used as the reference DIAMOND database for annotating environment sequences from eukaryotic metatranscriptomes. <br><br></p>
A Danish high-resolution dataset for six office rooms with occupancy, indoor environment , heating, ventilation, lighting and room control monitoring
<p>A dataset containing measurement data for six office rooms in Aalborg Denmark.<br>All the measurements have been resampled to 5 minute resolution<br>The measurements consists of:</p> <ul> <li>BMS data for the rooms</li> <li>Occupancy for the rooms (from cameras)</li> <li>BMS data for the AHU supplying the rooms</li> <li>BMS data for the Heating system supplying the rooms</li> </ul> <p>Changes from v2<br>It was found that the pressure difference measurements across the exhaust fan was faulty and the following variables have therefore been removed:</p> <ul> <li>Ventilation:Fan__air_flow__exhaust</li> <li>Ventilation:Fan__pressure_difference__exhaust</li> </ul> <p>More data has been added, now increasing the dataset to span the rest of 2023. To better handle the changes between standard time and daylight-saving time the column named "timestamp" has been adjusted so the datetime format now follows the ISO 8601 format YYYY-MM-DDThh:mm:ss+hhmm. the +hhmm changes between 0100 (Danish standard time) and 0200 (Danish daylight-saving time).</p> <p> </p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
Data for: Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network
<p>The data is supplementary to the publication "Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network", DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00406">10.1021/acs.jpclett.4c00406</a></p> <p>Key words: Thermal volume expansion, Interphase dynamics, Temperature-modulated optical refractometry, Nanoparticles, Optical Remanence, Hysteresis, Refractive index</p> <p>The data sets contain measured and processed data on the interphase dynamics of a nanoparticle modified epoxy resin collected via Temperature-modulated optical refractometry (TMOR).</p> <p>Material details:</p> <ul> <li>Cycloaliphatic epoxy resin + Anhydride curing agent + 1-methylimidazole</li> <li>Core-shell rubber nanoparticles, 100 nm, dispersed in a cycloaliphatic epoxy carrier resin</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> </ul>
Controlling the Dewetting Morphologies of Thin Liquid Films by Switchable Substrates
<p>Data and scripts for the creation of the data used in the publication: "Controlling the dewetting morphologies of thin liquid films by switchable substrates" in Phys. Rev. Fluids.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Dataset of the paper "Control of electronic band profiles through depletion layer engineering in core-shell nanocrystals"
<p>This dataset provides the raw data of the paper "Control of electronic band profiles through depletion layer engineering in core-shell nanocrystals"</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</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>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Myosin turnover controls actomyosin contractile instability
<p>Simulation and experimental data related to the preprint "Myosin turnover controls actomyosin contractile instability" (https://www.biorxiv.org/content/10.1101/2021.03.18.436017). </p>
Experimental HIL datasets of a heat pump controlled by MPC or rule-based controllers for energy flexibility
<p>Hardware-in-the-loop experiment performed in the SEILAB laboratory of IREC<br> Air-to-water heat pump including a DHW tank for production of SH and DHW, which external unit is placed in a climate chamber that reproduces the desired weather conditions dynamically<br> Control is MPC or rule-based, both triggered either by a signal of price or CO2 intensity from the grid (4 series of experiments)<br> Connected to virtual residential building (flat) in Spanish Mediterranean climate<br> More information:<br> https://doi.org/10.1109/ACCESS.2019.2903084</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.