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80,290 results for “revision”
Datasets and R-scripts used for the revision of the Dibrachys cavus complex by Peters & Baur, 2011, Zootaxa 2937.1
<p>In 2011 we published a revision on the Dibrachys cavus complex in Zootaxa (Peters and Baur, 2011, here a link to our <a href="https://doi.org/10.11646/zootaxa.2937.1.1">open access paper</a>). It was our wish to also publish two versions of the dataset (one with missing values, one with missing values imputed) as supplementary files. Unfortunately, the data files seem to be no longer available on the publishers webpage. Hence, we publish the data files herewith again in CSV format.</p> <p>We take the opportunity to also publish the R-scripts that we used for calculating multivariate analyses, tests, and the multiple imputation of missing values.</p> <p>All files are available individually and with an own link. For convenience, we have compiled all files also in a ZIP file.</p> <p><a href="https://doi.org/10.5281/zenodo.4256704">Baur (2020)</a> used the dataset for further exploration in a Multivariate Ratio Analysis (MRA).</p> <p>Papers quoted above you may find in the section <em>References</em> of the Zenodo package.</p> <p><strong>Citation of this package</strong><br> Peters, Ralph S., & Baur, Hannes (2020, November 9) Datasets and R-scripts used for the revision of the Dibrachys cavus complex by Peters & Baur, 2011, Zootaxa 2937.1. Zenodo. https://doi.org/10.5281/zenodo.4264539 (directs to the newest version of the package).</p>
The portrayal of underlings in Eastern Cālukya copper plates: textual analysis data with revised codebook
<p>This is a textual analysis dataset derived from Eastern Cālukya copperplate grants. The second version of 24 April 2024 contains a slightly revised codebook (in DOC and PDF formats with identical contents) in addition to the earlier dataset. This revision is essentially identical to that reflected in the "Revised tag" column of the dataset, except that many definitions have been made clearer and tidier, and a small number of intermediate-level categories have been added for better hierarchisation. The codes in use have not been altered.</p> <p><br>The data accompany the following forthcoming publications (title and date of publication subject to change):</p> <p>Balogh, Dániel (forthcoming 2024), 'The portrayal of underlings in Eastern Cālukya copper plates'. In: Self-Representation and Presentation of Others in Indic Epigraphical Writing, edited by Annette Schmiedchen and Dániel Balogh. Wiesbaden: Harrassowitz.</p> <div> <div>Balogh, Dániel (forthcoming 2024). ‘Textual Analysis Methodology and Royal Representation in Copperplate Grants’. In <em>Bhūtārthakathane ... Sarasvatī: Reading Poetry as a History Book</em>, edited by Marco Franceschini, Chiara Livio, and Lidia Wojtczak. Studies on the History of Śaivism. Naples: UniorPress.</div> </div> <p>The former publication introduces the method sketched out on the introductory page of this dataset and studies a particular topic through this methodology, while the latter discusses the method in more detail. An account of the technical details is in preparation by Balogh.</p> <p><br>This dataset, the underlying research and the relevant publications are results of the project DHARMA ‘The Domestication of “Hindu” Asceticism and the Religious Making of South and Southeast Asia’. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 809994).</p>
S13 | EUCOSMETICS | Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)
<p>This is the collection associated with list S13 EUCOSMETICS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p><strong>Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)</strong></p> <p>Merged Cosmetics <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017.csv">CSV</a> (4/05/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/eucosmetics">EU Cosmetics List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017_InChIKeys.txt">Merged Cosmetics InChIKeys</a> (4/05/2017)</p> <p>The scientific committee on cosmetic products and non-food products Intended for consumers - <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/SCCNFP038900_INCI-2000.pdf">SCCNFP/0389/00 Final</a> and Commission <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Decision_2006_257_EC.pdf">Decision 2006/257/EC</a> amending the Decision 96/335/EC. Provided by Peter von der Ohe, UBA, curated by Reza Aalizadeh, University of Athens.</p> <p>Update 12 May 2020: Added the source data from both reports (INCI-2000 and Decision 96/335/EC), including the function information and undefined structures. Update 28 May 2020: removed incomplete structural information from Decision 96/335/EC files. Update 24/7/2020: corrected one synonym reported by PubChem (GFMHHNOUDDHEOO-UHFFFAOYSA-N).</p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Updated Files from the revision 1 (986 cMAGs and prokka annotations, all supplemental files)
<p>Included are the following files<br><br>1) Assembly files (47) in the form of .fasta for each of the 47 different samples (8 participants x 5 or 6 time points). This is the results of the assembly from hybrid long read data (all Pacbio Revio + ONT Promethion > Q20). Assemblies performed using metaMDBG. This file includes all contigs from the pipeline described in the manuscript and will include both high-quality and complete MAGs. <br>"hybrid_assemblies.tar.gz"<br><br>2) Assembly files (40) in the form of .fasta for the short read metaspades and tell-seq assembly methods<br>"SR_tellseq_assemblies.tar.gz"<br>"SR_metaspades_assemblies.tar.gz"</p> <p>3) Assembly files from sub-sampling experiment: 3 samples (A6, D5, and H6) were deep sequenced with both PB Revio and ONT Promethion lsk114 R10.4.1 SUP400. Data was randomly subsampled to various depths (1, 5, 10, 20, 30, 40 Gbp, and all) and then assembled with both metaMDBG and metaFlye. <br>-A6: total of 26 files (PB was less than 40 Gbp thus no 40 Gbp subsampling depth)<br>-D5: total of 28 files<br>-H6: total of 28 files<br>-total: 82 files<br>"LR_ONTPB_sub_assemblies.tar.gz"</p> <p>4) Tar file of all circular contigs from assemblies. This will include 47 separate fasta files (1 for each participant and time point). This data was used for the viral and plasmid analyses. <br>"Hsap_circ_contigs_47_assemblies.tar.gz"<br><br>5) File containing all of the 985 cMAGs with annotations generated using prokka</p> <p>"985cMAGs_prokkaannotation.tar.gz</p> <p>6) Deep taxonomic profiling feature table: Pacbio samples (45) with features classified using the GTDB and 985 cMAGs custom database<br>"PB_985cMAG-sourmash_45s_2162f" #feature table<br>"mapping-file_PB45s_2162f_deID.txt" #mapping file, deidentified<br><br>7) tar file containing all genomes in the updated quality 'c986 cMAGs' <br>(>90% completeness, <5% contamination, contig=1<br><br>8) tar file containing all prokka annotation files associated with the 986 cMAGs<br><br>9) All supplemental tables or data files used in the revision</p>
Dataset for: A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993–2019
<p>Version 5 changes the variable names in the NetCDF file so that are more readable by xarray.</p> <p>The NetCDF v4 file (Greenland_radiostratigraphy_v2.nc) contains both the gridded depths of synthetic isochrones (i.e., at ages of interest that were not necessarily directly observed) and the age at regular normalized depths (10–80%) in the ice sheet.</p> <p>For each campaign that was traced, there is an HDF5-compliant .mat MATLAB file (Greenland_radiostratigraphy_v2_X_Y, where X is the year the campaign was flown and Y is the aircraft that was used) that contains the original traced and dated reflections for each traced segment in that campaign. If using Python instead of MATLAB, the mat73 package and its loadmat function can be used to load these files. An example Jupyter notebook is included that illustrates how to access the full contents of .mat file using Python and this package. There is also a zipped (.zip) archive for each campaign that contains GeoPackage (.gpkg) files for each traced segment of that campaign. Because of format restrictions, these GeoPackages only include the traced reflections' depths, with the ages in the metadata. </p> <p> </p>
Data set of 'Adiabatic temperature profile in the mantle, revised'
<p>P-V-T data of the four major mantle minerals, olivine, wadselyite, ringwoodite, and bridgmanite</p> <p>The original data are as follows:</p> <p>Olivine: https://doi.org/10.1016/j.pepi.2008.08.002</p> <p>Wadsleyite: https://doi.org/10.1029/2009GL038107</p> <p>Ringwoodite: https://doi.org/10.1029/2004JB003094</p> <p>Bridgmanite; https://doi.org/10.1029/2009GL039318 https://doi.org/10.1029/2011JB008988</p> <p>The temperatures were recalculated using https://doi.org/10.1016/j.pepi.2019.106348</p> <p>The pressures were recalculated using https://doi.org/10.1029/2011JB008988</p> <p> </p>
Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC
<p>This dataset contains the revised version of the SISLAC database in three formats: comma-separated values (.csv), microsoft access (.mdb) and PostGIS database (.backup). This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected. Consists of two tables, one for the description of the profiles and the other with the description of the horizons and their properties. The key field between both tables is the profile identifier, column <strong><em>profile_id</em></strong>.</p>
BRAVES database Version 1.1 (REVISED): multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil.
<p>The BRAzilian Vehicular Emissions inventory Software (BRAVES) database is a multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil. We provide this database using a spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on SPECIATE database from the United States Environmental Protection Agency. We provide netCDF files with spatial resolution of 0.05x0.05 and annual emissions from 2013 to 2019. Files are divided by vehicle type (light, commercial-light, motorcycles, and heavy). We also provide the total vehicular emissions (sum of emissions from all vehicle types). The database contains emissions of 41 chemical species, such as ACET, ACROLEIN, ALD2, BENZ, BUTADIENE13, CH4, CO, CO2, ETH, ETHA, ETHY, ETOH, FORM, ISO, N2O, NAPH, NO, NO2, PAL, PCA, PCL, PEC, PFE, PK, coarse mode primary PM (PMC), PMG, PMN, unspeciated PM2.5 (PMOTHR), PNA, PNH4, PNO3, POC, PRPA, PSI, PSO4, PTI, SO2, TERP, TOL, VOC, and XYLMN. Codes from BRAVES are available by registering at <a href="https://hoinaski.prof.ufsc.br/BRAVES/">https://hoinaski.prof.ufsc.br/BRAVES/</a> and <a href="https://github.com/leohoinaski/BRAVES">https://github.com/leohoinaski/BRAVES</a>, where users can access instructions to run the database and download the input files.</p> <p>In this updated version from the first BRAVES database version, we have preserved estimates of ETOH and RCHO from CETESB. Brazil has a unique chemical signature of the chemical composition due to the biofuels (27% of gasoline is ethanol and 7% of diesel is bio-diesel). The emissions of C2H4O (ALD2), CH2O (FORM), and C3H6O (ACET) have been derived from RCHO emissions. We have used US-EPA Speciate to speciate compounds only when local emission factors of RCHO and ETOH are not available, such as in the case of motorcycles and heavy vehicles.</p> <p>We have also included emissions of PM2.5 (PMFINE), speciating coarse PM emissions from brake and tires (40%), road wear (53%), road dust resuspension (17%), and exhaust emissions (100%). Pixel center coordinates (longitude, latitude), pixel area (AREA), and pixel local time zone (LTZ) shift from UTC has been added to the netCDF files.</p> <p>This database has been currently part of the preprint currently under review for the journal ESSD (https://doi.org/10.5194/essd-2022-74).</p> <p> </p> <p>Files description:</p> <p>BRAVESdatabaseAnnual_BR_(typeEmiss)_(Vehicle Type)_(resolution)_(year).nc - Annual emissions in Brazil by vehicle type and 0.05x0.05 degree of resolution.</p> <p>Domain:</p> <p>lati = -36 #(Brazil) #lati = int(round(bound.miny)) # Initial latitude</p> <p>latf = 8 #(Brazil) #latf = int(round(bound.maxy)) # Final latitude</p> <p>loni = -76 #(Brazil) #loni = int(round(bound.minx)) # Initial longitude</p> <p>lonf = -32 #(Brazil) #lonf = int(round(bound.maxx)) # Final longitude</p> <p>deltaX = 0.05 # Grid resolution/spacing in x direction</p> <p>deltaY = 0.05 # Grig resolution/spacing in y direction</p> <p> </p> <p>typeEmiss:</p> <p>'TOTAL' = Total emissions/sum of emissions types<br> 'Exhaust' = Only exhaust emissions<br> 'non-exaust' = Only non-exhaust emissions<br> 'non-exaustMP' = Only Particulate Matter non-exhaust emissions<br> 'non-exaustMP_no_resusp'= Only Particulate Matter non-exhaust emission excluding road resuspension </p>
CLDF dataset derived from Chacon's "A revised proposal of Proto-Tukanoan consonants and Tukanoan family classification" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>Thiago Chacon. (2014). A revised proposal of Proto-Tukanoan consonants and Tukanoan family classification. Journal of American Linguistics 80.3, pp. 275–322. doi: https://doi.org/10.1086/676393</p> </blockquote>
IN02037 Khopasi Stone Inscription (revise title). Sanskrit XML file, draft epidoc edition
<p>IN02037 Khopasi Stone Inscription (revise title). Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
IN02036 Dharampur Stone Inscription 2 (revise title). Sanskrit XML file, draft epidoc edition
<p>IN02036 Dharampur Stone Inscription 2 (revise title). Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
IN02088 Thimi Stone Inscription (revise title). Sanskrit XML file, draft epidoc edition
<p>IN02088 Thimi Stone Inscription (revise title). Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
IN02083 Kathmandu Black Stone Inscription (revise title). Sanskrit XML file, draft epidoc edition
<p>IN02083 Kathmandu Black Stone Inscription (revise title). Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p> <p> </p>
IN02081 Sanku Fragment Inscription (revise title). Sanskrit XML file, draft epidoc edition
<p>IN02081 Sanku Fragment Inscription (revise title). Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
caseysaenger/ForamMgCa_PSM: files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data".
<p>files and scripts for revised version of manuscript "Calibration and validation of environmental controls on planktic foraminifera Mg/Ca using global core-top data". Saenger, C. and M. N. Evans. Resubmitted to Paleoceanography and Paleoclimatology, May 3, 2019.</p>
Revised marine fossil record of the Mediterranean before and after the Messinian Salinity Crisis
<p>This is a unified and revised marine fossil record of the Mediterranean covering the Tortonian stage, the pre-evaporitic Messinian and the Zanclean stage and encompassing 23032 occurrences of calcareous nannoplankton, dinoflagellates, foraminifera, corals, ostracods, bryozoans, echinoids, mollusks, fishes, and marine mammals. It consists of four files in .csv format: 1) 'MessinianDB' contains the fossil occurrences; 2) 'coord' has the list of fossiliferous localities with their coordinates and the groups of organisms reported from each one; 3) 'DBrefs' contains the full citations of the references in the database; 4) 'corals' contains the list of coral genera in the database, indicating whether or not they include zooxanthellate (z-corals) or azooxanthellate (az-corals) species, or both. In the latter case, we further indicate if the species found in the database should be considered z- or az-corals, based on the accompanying fauna. </p>
Evolution of FDA Guidelines on Control of Nitrosamine Impurities in Human Drugs – A Comparative Analysis of September 2024 Revisions
<p>Nitrosamine impurities have become a significant concern in the pharmaceutical industry due to their carcinogenic potential. In response, the U.S. Food and Drug Administration (FDA) has continuously updated its guidelines to ensure the safety and efficacy of drug products. This review article provides a comprehensive analysis of the evolution of FDA guidelines on the control of nitrosamine impurities, with a particular focus on the September 2024 revisions. By comparing the latest guidance with previous versions, this article highlights key changes, including the expanded focus on Nitrosamine Drug Substance-Related Impurities (NDSRIs), updated risk assessment strategies, and the introduction of new Acceptable Intake (AI) limits. The analysis underscores the FDA's commitment to enhancing drug safety through rigorous control measures and global harmonization efforts.</p>
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>
dictionaria/guarayu: Guarayu. A revised dictionary by Alfred Hoeller
Danielsen, Swintha and Sell, Lena and Terhart, Lena. 2019. Guarayu. A revised dictionary by Alfred Hoeller. Dictionaria 7. 1-3590 (Available online at https://dictionaria.clld.org/contributions/guarayu)
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.