The NIST Computational Chemistry Comparison and Benchmark Database is a collection of experimental and ab initio thermochemical properties for a selected set of gas-phase molecules. The goals are to provide a benchmark set of experimental data for the evaluation of ab initio computational methods and allow the comparison between different ab initio computational methods for the prediction of gas-phase thermochemical properties. The data files linked to this record are a subset of the experimental data present in the CCCBDB.

Explore the booming Database Comparison Software market, projected to reach USD 235 million by 2025 with a 7.8% CAGR. Discover key drivers like cloud adoption and data integrity needs.

Iodoacetamide is perhaps the most widely used reagent for the alkylation of free sulfhydryls in proteomic experiments. Here, we report that both incomplete derivatization of Cys side chains and overalkylation of the peptides may lead to the misassignment of glycoforms when LC–MS/MS with electron-transfer dissociation (ETD) alone is used for the structural characterization of glycopeptides. Accurate mass measurements do not help, because the elemental compositions of the misidentified and correct modifications are identical. Incorporation of “higher-energy C-trap dissociation” (HCD), i.e., beam-type collision-induced dissociation data into the database searches with ETD data may prove decisive in most cases. However, the carbamidomethylation of Met residues leads to sulfonium ether formation, and the resulting fixed positive charge triggers a characteristic fragmentation, that eliminates the normal Y1 fragment from the HCD spectra of N-linked glycopeptides, producing an abundant Y1-48 Da ion instead (the nominal mass difference is given relative to the unmodified amino acid sequence), that easily can be mistaken for the side chain loss from Met sulfoxide. In such cases, good quality ETD data may indicate the discrepancy, and will also display abundant fragments due to CH3–S–CH2CONH2 elimination from the charge-reduced precursor ions. Our observations also draw attention to the underreported interference of different unanticipated covalent modifications.

The Database Comparison Software market is booming, projected to hit $222.3 million in 2025 and grow at a CAGR of 7.5% to 2033. Discover key trends, leading companies (Red Gate, dbForge, etc.), and regional market insights in this comprehensive analysis. Explore cloud-based vs. on-premise solutions and the impact of DevOps on this rapidly expanding sector.

The nutrition values of fish and other aquatic foods have recently gained global recognition for their potential to alleviate ‘hidden hunger’ in many contexts and for many nutritional vulnerable people. Yet, data for most fish, aquatic species, and forms of aquatic foods (particularly those of lower commercial value) are unavailable and unattainable due to the prohibitive cost of high-quality nutrient analysis. This means the databases that house the data that do exist are simultaneously incredibly valuable and riddled with gaps. Many initiatives have risen to address this challenge of compiling the best quality, to all available, data on the nutrient qualities of fish and other aquatic foods. There are multiple databases that now exist through which a researcher or policy maker might locate or contribute data. These include (1) Analytical Food Composition Database; (2) Food Composition Database for Biodiversity Global food composition database for fish and shellfish (3) Seafood Data (4) FishNutrients (5) Aquatic Food Composition Database (6) FoodEXplorer (7) the many different National Food Composition Databases. With input from experts from the fields of food sciences, nutrition and fisheries, and with a rapid review process by database curators, we compiled the metadata for seven different databases that contain large data set on nutrient qualities of fish and other aquatic foods. By summarising metadata, and generating a comparison between databases, we envisage that this tool will help researchers navigate these different tools, and better understand their different strengths and limitations.

PIECE is a plant gene structure comparison and evolution database with 25 species. Annotated genes extracted from the species are classified based on the Pfam motif and phylogenetic trees are reconstructed for each gene category integrating exon-intron and protein motif information. Resources in this dataset:Resource Title: Web Page. File Name: Web Page, url: https://probes.pw.usda.gov/piece/index.php

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developed using satellite imagery, including the Normalized Difference Vegetation Index (NDVI). The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a multi-band raster stack of monthly NDVI images from January 2014 through July 2022 covering the area of the Bylas Fire. We included data from 2022, contrasting the full study which only includes data through 2021, to include additional data regarding our post-fire vegetation response analysis. Each band within the raster stack represents a month from 2014 through 2022 (i.e., band 1 is January 2014 and band 103 is July 2022).

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2020, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

This replication package contains the data and code necessary to replicate the tables and figures in the related publication. For a pre-print version of this publication, see: https://www.rug.nl/ggdc/html_publications/memorandum/gd195.pdf. For a description of this package, please see the Readme.docx document; the TablesFigures.do file is the master Stata file that can be used to generate all material in Stata 18. The sector-level PPPs are part of the GGDC Productivity Level Database version 2023, separately available via https://doi.org/10.34894/AEAX1F.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

The data we used for this study include species occurrence data (n=15 species), climate data and predictions, an expert opinion questionnaire, and species masks that represented the model domain for each species. For this data release, we include the results of the expert opinion questionnaire and the species model domains (or masks). We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range. The species masks, or model domains, were defined separately for each species using a variation of the “target-group” approach (Phillips et al. 2009), where the domain was determine using convex polygons including occurrence data for at least three phylogenetically related and similar species (Watling et al. 2012). The species occurrence data, climate data, and climate predictions are freely available online, and therefore not included in this data release. The species occurrence data were obtained primarily from the online database Global Biodiversity Information Facility (GBIF; http://www.gbif.org/), and from scientific literature (Watling et al. 2011). Climate data were obtained from the WorldClim database (Hijmans et al. 2005) and climate predictions were obtained from the Center for Ocean-Atmosphere Prediction Studies (COAPS) at Florida State University (https://floridaclimateinstitute.org/resources/data-sets/regional-downscaling). See metadata for references.

Information

The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144803 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design.

The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation.

Structure and content of the dataset

Dataset structure

ChEMBL ID	PubChem ID	IUPHAR ID	Target	Activity type	Assay type	Unit	Mean C (0)	...	Mean PC (0)	...	Mean B (0)	...	Mean I (0)	...	Mean PD (0)	...	Activity check annotation	Ligand names	Canonical SMILES C	...	Structure check	Source

The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file.

Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format.

Column content:

• ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases
• Target: biological target of the molecule expressed as the HGNC gene symbol
• Activity type: for example, pIC₅₀
• Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified
• Unit: unit of bioactivity measurement
• Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database
• Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence
  • no comment: bioactivity values are within one log unit;
  • check activity data: bioactivity values are not within one log unit;
  • only one data point: only one value was available, no comparison and no range calculated;
  • no activity value: no precise numeric activity value was available;
  • no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration
• Ligand names: all unique names contained in the five source databases are listed
• Canonical SMILES columns: Molecular structure of the compound from each database
• Structure check: To denote matching or differing compound structures in different source databases
  • match: molecule structures are the same between different sources;
  • no match: the structures differ;
  • 1 source: no structure comparison is possible, because the molecule comes from only one source database.
• Source: From which databases the data come from

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2022 and single recent year data pertain to citations received during calendar year 2022. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work.

PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases.

The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918).

Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

This public database is designed for evaluation of carrier frequency difference estimation systems and is published as part of our paper "Open Range Pitch tracking for Carrier Frequency Difference
Estimation from HF Transmitted Speech". It consists of over 23 ours of real transmissions over HF links with a known carrier frequency shift during demodulation.

To record the data we have set up a transmission system between our base station in Paderborn and several other distant base stations across Europe (see Fig. 4), transmitting utterances from the LibriSpeech corpus.

Kiwi-software defined radio (SDR) devices at distant base stations were utilized to demodulate the received SSB HF signals and send the recorded audio signals back to our servers via a websocket connection. Audio markers had been added to the transmitted signal to allow for an automated time alignment between the transmitted
and received signals, easing the annotation and segmentation of the data.
For the transmissions a beacon, callsign DB0UPB, was used, which was supervised by a human to avoid interference with other ham radio stations. The HF signals are SSB modulated using the Lower Side Band (LSB) with a bandwidth of 2.7 kHz at carrier frequencies of 7.06 MHz − 7.063 MHz and 3.6 MHz − 3.62 MHz. To simulate a carrier frequency difference the demodulation frequency of the transmitter and the receiver were selected to differ by values from the set [0, 100, 300, 500, 1000]. Although the original speech samples have a sampling rate of 16 kHz, and the Kiwi-SDR samples the data at 12.001 Hz, the finally emitted data is band-limited to 2.7 kHz (International Telecommunication Union (ITU) regulations) which introduces a loss of the upper frequencies in case of LSB
transmission depending on the carrier frequency difference. The data set has a total size of 23:31 hours of which 3:28 hours contain speech activity.

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280. Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.

This data package contains information regarding CAHPS Comparison for Top Box Scores by Population and Adult Survey 2 Top Box Scores by Specialty and State. It provides data over CAHPS Practice Site Respondents by Region Ownership Mode and Provider, Health Plan Samples by State and HCAHPS of National and State Averages. It also contains dataset for Hospital Consumer Assessment of Providers and Patient Survey and Maryland’s Quality-Based Reimbursement (QBR) program for the fiscal year 2014.

Comprehensive database of 4.2M+ U.S. motor carriers with safety ratings, insurance renewal dates, VIN-decoded fleet equipment details, and DOT compliance data. Updated multiple times daily from FMCSA and insurance filings.

Slovenia Energy Balance: Statistical Difference data was reported at 5.661 TOE th in 2023. This records a decrease from the previous number of 6.916 TOE th for 2022. Slovenia Energy Balance: Statistical Difference data is updated yearly, averaging -13.228 TOE th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 19.163 TOE th in 2002 and a record low of -25.910 TOE th in 2004. Slovenia Energy Balance: Statistical Difference data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.RB001: Energy Balance.

The evaluation in catchability of egg and larval fish with the 0.61-m Bongo and the 1-m2 Multiple Opening and Closing Net Environmental Sensing System (MOCNESS) was conducted as part of a paired station analysis. The two samplers were deployed on 331 stations on Georges Bank during the US GLOBEC program from January through June for years 1996 and 1997. Significant differences were found when...

This functionality is primarily used by health policy researchers and the media. The data provided in the tables come from the data that is displayed in the Tool and includes additional information about the ownership that is not displayed on the website.The date Modified in the zipped file indicates the date of the last refresh of the data. For information about Facilities and Vendors in a particular geographical area, you should use the Compare tool instead of downloading the data. The followings tools are represented, Dialysis Compare Tool, Helpful Contacts, Home Health Compare, Hospital Compare, Medicare Options Compare, Nursing Home Compare, Plans Quality Data, and Supplier Directory.