Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.

1. Composite Beam Database v1.0

A database of composite steel beams that are part of moment-resisting frames is provided. The database consists of 97 tests conducted over the last 30 years. The collection and metadata methodology are thoroughly presented in El Jisr et al. (2019).

Each column in the spreadsheet is defined in the "Definitions" tab along with accompanying figures in the "Figures" tab. The database includes details of the composite slab (dimensions, material strength, shear studs) as well as the calculation of the plastic moment resistance and elastic stiffness of the sections as per European, US and Japanese provisions. A comparison between the code-based and test values is also shown. Furthermore, the database includes the plastic deformation capacity of the sections based on the first cycle envelope.

2.Digitized Moment Rotation Data v1.0

Full digitized histories of the moment-chord rotation of the composite beams are provided.

KEGG LIGAND contains knowledge of chemical substances and reactions that are relevant to life. It is a composite database consisting of COMPOUND, GLYCAN, REACTION, RPAIR, and ENZYME databases, whose entries are identified by C, G, R, RP, and EC numbers, respectively. ENZYME is derived from the IUBMB/IUPAC Enzyme Nomenclature, but the others are internally developed and maintained. The primary database of KEGG LIGAND is a relational database with the KegDraw interface, which is used to generated the secondary (flat file) database for DBGET.

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 and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added in the most recent iteration. 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-2023 and single recent year data pertain to citations received during calendar year 2023. 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 (7) is based on the August 1, 2024 snapshot from Scopus, updated to end of citation year 2023. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2024. 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, see attached file on FREQUENTLY ASKED QUESTIONS. 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 document lists all freely available data on thin-walled axially loaded composite cylindrical shells. The list includes the material used, the laminate lay-up, the wall thickness, the radius, the length, the determined material parameters, the boundary conditions, the buckling load, the manufacturer and manufacturing process as well as the test rig used.

If you have new test data you want to add in this database feel free to contact us via tobias.hartwich@tuhh.de or stefan.panek@tuhh.de

This database summarizes 165 experimental test data on beam-to-column connections for composite special moment frames (C-SMFs).

Mechanical Properties of Composite Materials for Airborne Wind Energy Kites

This database presents mechanical properties of composite materials tested at Composites Testing Laboratory (CTL Tástáil Teo.).

This database was created as part of the HAWK project funded by the Sustainable Energy Authority of Ireland (SEAI) (Award number: 22/RDD/893).

One of the aims of the HAWK project was to address the use of industrial-grade composite materials in Airborne Wind Energy (AWE) systems.

In this database, novel composite material systems were selected to add to the publicly available material data in databases such as the OptiDAT, SNL/MSU/DOE and NCAMP.

The selection process for materials sought to strike a balance between pragmatism and a consideration for sustainability. This has resulted in the selection of materials with natural fibres, novel recyclability and low-cost/high production characteristics which may provide a competitive edge and a sustainable future when applied to AWE systems.

These materials were selected with the AWE industry in mind but could be equally suited for use in other industries.

Mechanical Properties of Composite Materials for Airborne Wind Energy Kites

This database presents mechanical properties of composite materials tested at Composites Testing Laboratory (CTL Tástáil Teo.).

This database was created as part of the HAWK project funded by the Sustainable Energy Authority of Ireland (SEAI) (Award number: 22/RDD/893).

One of the aims of the HAWK project was to address the use of industrial-grade composite materials in Airborne Wind Energy (AWE) systems.

In this database, novel composite material systems were selected to add to the publicly available material data in databases such as the OptiDAT, SNL/MSU/DOE and NCAMP.

The selection process for materials sought to strike a balance between pragmatism and a consideration for sustainability. This has resulted in the selection of materials with natural fibres, novel recyclability and low-cost/high production characteristics which may provide a competitive edge and a sustainable future when applied to AWE systems.

These materials were selected with the AWE industry in mind but could be equally suited for use in other industries.

This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach. Aluminium composite panels consist of a layer of foam insulation, sandwiched between two aluminium sheets. Aluminium sheets are chosen for their durability, resistance to corrosion, large colour palette and strength. The foam is typically polyethylene or polyurethane. Rolled aluminium coils are used to sandwich the foam insulation, which is also fed to the manufacturing line as a roll. Adhesives are used to glue the aluminium sheets to the core. Aluminium composite panels are typically used as cladding. The panel specified here is 4 mm thick.

BackgroundStatins are the gold standard in the treatment of dyslipidemia, significantly reducing the risk of cardiovascular disease.ObjectiveTo systematically review the efficacy and safety of Moderate-intensity Rosuvastatin Plus Ezetimibe compared with High-intensity Rosuvastatin in treating Composite Cardiovascular Events.MethodsPubMed, Embase, Cochrane Library, CINAHL, Web of Science, China Knowledge Network, China Biological Literature Database, Wan Fang Database, and Weipu Database were searched to retrieve randomized controlled trials assessing the safety and efficacy of the two therapies from the time of construction to December 2023. The Jadad scale assessment tool was used to evaluate the quality of the included literature, and Review Manager 5.4 software was used for meta-analysis. The heterogeneity of outcomes was estimated by the I2 test, where we applied risk ratios (RR) and 95% confidence intervals (CI) to assess dichotomous outcomes and mean difference (MD) and 95% CI to present continuous outcomes. We used funnel plots to assess study publication bias and sensitivity analysis was used to address significant clinical heterogeneity.ResultsThe meta-analysis described 21 RCTs involving 24592 participants. The findings indicated that moderate-intensity statin combination therapy improved low-density lipoprotein cholesterol (LDL-C) (MD -8.06, 95% CI [-9.48, -6.64] p < 0.05), total cholesterol (TG) (MD -5.66, 95% CI [-8.51, -2.82] p < 0.05), and non-high-density lipoprotein cholesterol (non-HDL-C) (MD -17.04, 95% CI [-29.55, -4.54] p < 0.05) to a greater extent and superior in achieving LDL-C

Italy Exports of composite paper and paperboard (in rolls or sheets) to Egypt was US$13.77 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Italy Exports of composite paper and paperboard (in rolls or sheets) to Egypt - data, historical chart and statistics - was last updated on November of 2025.

The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS-SPSD composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.
The gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data.
Values range from 10 to 100 percent. Histosols (minus folists for this dataset) have a high content of organic matter and no permafrost. Most are saturated year round, but a few are freely drained. Histosols are commonly called bogs, moors, peats, or mucks.
https://www.nrcs.usda.gov/resources/data-and-reports/gridded-national-soil-survey-geographic-database-gnatsgo

Dataset_1 provides seven FASTA files corresponding to protein databases. The composite database, named “All_Databases_5950827_sequences.fasta” contains protein sequences retrieved from public databases related to cephalopods salivary glands and proteins identified from our original data. This database comprises a total of 5,950,827 protein sequences and in turn it is composed by six smaller databases, named with capital letters from A to F: Database_A_19087_sequences.fasta, Database_B_16990_sequences.fasta, Database_C_2427_sequences.fasta, Database_D_84778_sequences.fasta, Database_E_5106635_sequences.fasta, Database_F_720910_sequences.fasta. Each one of these databases, contains data from several sources, i.e.: Database_A_19087_sequences.fasta – protein database from proteogenomic analyses of O. vulgaris salivary apparatus, built by Fingerhut et al. (2018); Database_B_16990_sequences.fasta – antimicrobial peptides from a non-redundant database collected by Aguilera-Mendoza et al. (2015); Database_C_2427_sequences.fasta – proteins identified with Proteome Discoverer using our 12 LTQ raw files against the UniProt database for the Metazoa taxonomic selection (2018_07 release); Database_D_84778_sequences.fasta and Database_E_5106635_sequences.fasta – proteins identified, from de novo transcriptome assemblies of 16 cephalopods posterior salivary glands, by TransDecoder and six-frame translation tool, respectively; Database_F_720910_sequences.fasta – proteins obtained by six-frame translation tool using the transcripts profiled in the transcriptome of O. vulgaris, but not included by the authors in Database_A_19087_sequences.fasta.

The recycling of construction waste is crucial for environmental preservation and sustainable progress. By integrating construction waste into mortar production, substantial reductions in carbon emissions can be achieved. However, there is currently no dependable method for estimating mortar strength based on its composition. This paper explores the utilization of artificial intelligence technology to forecast the compressive strength of composite recycled mortar. Three artificial neural network (ANN) models are developed for this purpose. A comparison between predicted results and experimental findings demonstrates the ANN models' ability to reliably and robustly approximate mortar strength. Furthermore, utilizing extremum optimization through ANN's genetic algorithm function, the paper forecasts optimal compressive strength and corresponding mix proportions. Because of its high predictive accuracy, ANN can efficiently supplement conventional destructive tests, thereby conserving valuable time, resources, and capital within the construction industry. This research could significantly advance civil engineering by providing an optimal ANN model for predicting the compressive strength of composite recycled mortar, ultimately contributing to the reduction of carbon dioxide emissions and promoting environmental protection and sustainable development.

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the cadastral site (source DGFiP 2014)

Distribution format

These files are available in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally as github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: street number with suffix (e.g.: 1, 1BIS, 1D)
• street: street name
• post_code: 5-character postcode
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the cadastre, C+O = cadastre data enriched by OSM (road name for example)
• lat: latitude in WGS84 decimal degrees
• lon: longitude in WGS84 decimal degrees

updates, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A one-stop collaborative reporting/correction window will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions concerning the project or this dataset, you can contact bano@openstreetmap.fr

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the cadastral site (source DGFiP 2014)

Distribution format

These files are available in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally as github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: street number with suffix (e.g.: 1, 1BIS, 1D)
• street: street name
• post_code: 5-character postcode
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the cadastre, C+O = cadastre data enriched by OSM (road name for example)
• lat: latitude in WGS84 decimal degrees
• lon: longitude in WGS84 decimal degrees

updates, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A one-stop collaborative reporting/correction window will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions concerning the project or this dataset, you can contact bano@openstreetmap.fr

The Mainland China Composite Damaging Earthquake Catalog (MCCDE-CAT) was developed by Li et al. (2021). It contains three databases: Earthquake damage database, Intensity map database, Population exposure database, which for 493 damaging earthquakes that occurred in Mainland China during 1950-2019. Citation: "Y. Li, Z. Zhang, D. Xin, A Composite Catalog of Damaging Earthquakes for Mainland China, Seismol. Res. Lett. 92(6) (2021) 3767-3777. https://doi.org/10.1785/0220210090"

Predicting the final properties of new materials (composite materials). Composite material is a multicomponent material made from two or more components with significantly different physical and/or chemical properties that, when combined, result in a new material with characteristics that are different from those of the individual components and are not a simple superposition of them. It is customary to distinguish a matrix and fillers in the composition of a composite, the latter performing the function of reinforcement (by analogy with reinforcement in a composite building material such as reinforced concrete). The fillers of composites are usually carbon or glass fibers, and the role of the matrix is played by the polymer. The combination of different components improves the characteristics of the material and makes it both light and durable. At the same time, the individual components remain as such in the structure of the composites, which distinguishes them from mixtures and hardened solutions. By varying the composition of the matrix and filler, their ratio, and filler orientation, a wide range of materials with the required set of properties is obtained. Many composites are superior to traditional materials and alloys in their mechanical properties and at the same time they are lighter. The use of composites usually makes it possible to reduce the weight of a structure while maintaining or improving its mechanical characteristics. Modern composites are made from different materials: polymers, ceramics, glass and carbon fibers, but the basic principle remains the same. This approach also has a drawback: even if we know the characteristics of the original components, determining the characteristics of the composite consisting of these components is quite problematic. There are two ways to solve this problem: physical testing of material samples, and the second is predicting characteristics. The essence of forecasting is to simulate a representative element of the volume of the composite, based on data on the characteristics of the incoming components (binder and reinforcing component). Therefore, the relevance of the chosen topic is due to the fact that the created predictive models will help reduce the number of tests performed, as well as replenish the materials database with possible new characteristics of materials, and digital twins of new ones. In addition, an adequately functioning prediction model can significantly reduce the time, financial and other costs of testing. Therefore, it is necessary to develop models that predict tensile modulus and tensile strength, as well as a model that recommends the matrix-filler ratio.

The relevance lies in the fact that the created predictive models will help reduce the number of tests performed, as well as replenish the materials database with possible new characteristics of materials, as well as digital twins of new composites. Initial data on the properties of composite materials, presented in two data sets X_bp and X_nup.

The X_bp data set contains:

Matrix-filler ratio. Density, kg/m3. Modulus of elasticity, GPa. Amount of hardener, m.%. Content of epoxy groups,%_2). Flash point, C_2. Surface density, g/m2 Tensile modulus of elasticity, GPa Tensile strength, MPa Resin consumption, g/m2

The X_nup dataset contains:

Patch angle, degrees. Patch pitch. Patch density.

A comprehensive database of RWS connections has been assembled, encompassing data from 16 test and finite element (FE) programmes. The parameters include the primary attributes of the test programme, geometric properties, material characteristics, and response variables. The database was standardised using SI units to ensure consistency. This database encompasses both bare steel and composite RWS connections, as well as benchmarked solid webbed-beam connections. The database accounts for different types of test setups, namely, cantilever, cruciform and frame arrangements, with load and/or displacement is applied at the beam, or the column ends. Data for both welded and bolted extended end-plate connections are included, featuring configurations with three or four rows of bolts. Additionally, the database captures variations in steel and concrete cross-sectional geometries, nominal and measured material properties, from specifications of different countries. The descriptive statistics of the dataset, which consists of 801 RWS connection samples, covering a range of geometric, material, and mechanical properties. The dataset includes five connection types, with the Bolted Extended End-Plate (4 rows) configuration being the most common, followed by the Welded Connection, Pre-Northridge, Bolted Extended End-Plate (3 rows), and WUF-B. The geometric parameters show considerable variation, with web opening diameter (d_o) ranging from 0 to 525 mm, End-distance of a web opening (S_e) varying from 0 to 1675 mm (mean = 353.74 mm), and column height (h_c) ranging between 160 mm and 650 mm (mean = 362.12 mm). The flange thickness (t_bf) ranges from 8 mm to 28 mm, while the web thickness (t_bw) spans from 6 mm to 18 mm, influencing the capacity metrics of the RWS connections. The material properties also exhibit variability, with column yield strength (f_ynC) and beam yield strength (f_ynB) averaging 338.25 MPa and 336.69 MPa, respectively, while concrete compressive strength (f_c) varies widely from 0 to 38.08 MPa. In total, the database comprises 801 test specimens and FE models of RWS connections, including 20 benchmarked solid webbed-beam counterparts. Despite its extensive scope, a small number of experimental and FE programmes were excluded due to insufficient test or modelling details. This database represents a valuable resource for advancing the understanding and development of RWS connections for seismic-resistance structural design.

Denmark Imports of composite paper and paperboard (in rolls or sheets) from Serbia was US$178 during 2023, according to the United Nations COMTRADE database on international trade. Denmark Imports of composite paper and paperboard (in rolls or sheets) from Serbia - data, historical chart and statistics - was last updated on December of 2025.