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

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.

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

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including the current pandemic caused by COVID-19. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent candidates of unique substructures for fragment-based drug discovery inspired on natural products. To this end, fragment libraries are required that can be incorporated into automated drug design pipelines. However, it is still scarce to have public fragment libraries based on extensive collections of natural products. Herein we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of food chemical databases and other compound data sets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of contents and diversity.Sopporting information contains: COCONUT_COMPOUNDS.csv, FooDB_COMPOUNDS.csv, DCM_COMPOUNDS.csv, CAS_COMPOUNDS.csv, 3CLP_COMPOUNDS.csv. All datasets contain the curated structures and the following information: identicator number (ID), simplified molecular input line entry system (Smiles), Average Molecular Weight (AMW), number of carbons, oxygens, nitrogens, heavy atoms, aliphatic rings, aromatic rings, heterocycles, bridgehead atoms, fraction of sp3 carbon atoms and chiral carbons, and a list of fragments generated from each compound. FRAGMENTS_COCONUT.csv, FRAGMENTS_FooDB.csv, FRAGMENTS_DCM.csv, FRAGMENTS_CAS.csv, FRAGMENTS_3CLP.csv. All libraries contain structures generated (Fragments) from each compound library (Dataset) and the following information: number of compounds that contain that fragment in a dataset (Count) and fraction of them (Proportion), average Molecular Weight (AMW), number of carbons, oxygens, nitrogens, heavy atoms, aliphatic rings, aromatic rings, heterocycles, bridgehead atoms, fraction of sp3 carbon atoms and chiral carbons.

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.

Alphabetical Listing of Toxic Compound Databases.

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.

CSB.DB presents the results of bio-statistical analysis on gene expression data in association with additional biochemical and physiological knowledge. The main aim of this database platform is to provide tools that support insight into life''s complexity pyramid with a special focus on the integration of data from transcript and metabolite profiling experiments. The main focus of the CSB project is the generation of new easily accessible knowledge about the relationship and the hierarchy of cellular components. Thus new progress towards understanding lifes complexity pyramid is made. For this aim statistical and computational algorithms are applied to organism specific data derived from publicly available multi-parallel technologies, currently such as expression profiles. The underlying data are derived from various research activities. Thus CSB project provides an integrated and centralized public resource allowing universal access on the generated knowledge CSB.DB: A Comprehensive Systems-Biology Database. The derived knowledge should support the formulation of new hypotheses about the respective functional involvement of genes beyond their (inter-) relationships. Another major goal of the CSB project is to supply the researchers with necessary information to formulate these new hypotheses without demanding any a-priori statistical knowledge of the user. The CSB project mainly focuses on application of required statistical tests as well as to assist the user during exploration of results with information / help files to support hypothesis generation

Open source database used for analyzing and modeling compound interactions with human and animal organ models.Platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. Resource for relating in vitro organ model data to multiple biochemical, preclinical, and clinical data sources on in vivo drug effects.

The free database mapping COVID-19 treatment and vaccine development based on the global scientific research is available at https://covid19-help.org/.

Files provided here are curated partial data exports in the form of .csv files or full data export as .sql script generated with pg_dump from our PostgreSQL 12 database. You can also find .png file with our ER diagram of tables in .sql file in this repository.

Structure of CSV files

*On our site, compounds are named as substances

compounds.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the Substance/Compound (string)

Marketed name - The marketed name of the Substance/Compound (string)

Synonyms - Known synonyms (string)

Description - Description (HTML code)

Dietary sources - Dietary sources where the Substance/Compound can be found (string)

Dietary sources URL - Dietary sources URL (string)

Formula - Compound formula (HTML code)

Structure image URL - Url to our website with the structure image (string)

Status - Status of approval (string)

Therapeutic approach - Approach in which Substance/Compound works (string)

Drug status - Availability of Substance/Compound (string)

Additional data - Additional data in stringified JSON format with data as prescribing information and note (string)

General information - General information about Substance/Compound (HTML code)

references.csv

Id - Unique identifier in our database (unsigned integer)

Impact factor - Impact factor of the scientific article (string)

Source title - Title of the scientific article (string)

Source URL - URL link of the scientific article (string)

Tested on species - What testing model was used for the study (string)

Published at - Date of publication of the scientific article (Date in ISO 8601 format)

clinical-trials.csv

Id - Unique identifier in our database (unsigned integer)

Title - Title of the clinical trial study (string)

Acronym title - Acronym of title of the clinical trial study (string)

Source id - Unique identifier in the source database

Source id optional - Optional identifier in other databases (string)

Interventions - Description of interventions (string)

Study type - Type of the conducted study (string)

Study results - Has results? (string)

Phase - Current phase of the clinical trial (string)

Url - URL to clinical trial study page on clinicaltrials.gov (string)

Status - Status in which study currently is (string)

Start date - Date at which study was started (Date in ISO 8601 format)

Completion date - Date at which study was completed (Date in ISO 8601 format)

Additional data - Additional data in the form of stringified JSON with data as locations of study, study design, enrollment, age, outcome measures (string)

compound-reference-relations.csv

Reference id - Id of a reference in our DB (unsigned integer)

Compound id - Id of a substance in our DB (unsigned integer)

Note - Id of a substance in our DB (unsigned integer)

Is supporting - Is evidence supporting or contradictory (Boolean, true if supporting)

compound-clinical-trial.csv

Clinical trial id - Id of a clinical trial in our DB (unsigned integer)

Compound id - Id of a Substance/Compound in our DB (unsigned integer)

tags.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the tag (string)

tags-entities.csv

Tag id - Id of a tag in our DB (unsigned integer)

Reference id - Id of a reference in our DB (unsigned integer)

API Specification

Our project also has an Open API that gives you access to our data in a format suitable for processing, particularly in JSON format.

https://covid19-help.org/api-specification

Services are split into five endpoints:

Substances - /api/substances

References - /api/references

Substance-reference relations - /api/substance-reference-relations

Clinical trials - /api/clinical-trials

Clinical trials-substances relations - /api/clinical-trials-substances

Method of providing data

All dates are text strings formatted in compliance with ISO 8601 as YYYY-MM-DD

If the syntax request is incorrect (missing or incorrectly formatted parameters) an HTTP 400 Bad Request response will be returned. The body of the response may include an explanation.

Data updated_at (used for querying changed-from) refers only to a particular entity and not its logical relations. Example: If a new substance reference relation is added, but the substance detail has not changed, this is reflected in the substance reference relation endpoint where a new entity with id and current dates in created_at and updated_at fields will be added, but in substances or references endpoint nothing has changed.

The recommended way of sequential download

During the first download, it is possible to obtain all data by entering an old enough date in the parameter value changed-from, for example: changed-from=2020-01-01 It is important to write down the date on which the receiving the data was initiated let’s say 2020-10-20

For repeated data downloads, it is sufficient to receive only the records in which something has changed. It can therefore be requested with the parameter changed-from=2020-10-20 (example from the previous bullet). Again, it is important to write down the date when the updates were downloaded (eg. 2020-10-20). This date will be used in the next update (refresh) of the data.

Services for entities

List of endpoint URLs:

/api/substances

/api/references

/api/substance-reference-relations

/api/clinical-trials

/api/clinical-trials-substances

Format of the request

All endpoints have these parameters in common:

changed-from - a parameter to return only the entities that have been modified on a given date or later.

continue-after-id - a parameter to return only the entities that have a larger ID than specified in the parameter.

limit - a parameter to return only the number of records specified (up to 1000). The preset number is 100.

Request example:

/api/references?changed-from=2020-01-01&continue-after-id=1&limit=100

Format of the response

The response format is the same for all endpoints.

number_of_remaining_ids - the number of remaining entities that meet the specified criteria but are not displayed on the page. An integer of virtually unlimited size.

entities - an array of entity details in JSON format.

Response example:

{

"number_of_remaining_ids" : 100,


"entities" : [


  {


    "id": 3,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32147628",


    "title": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "impact_factor": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "tested_on_species": "in silico",


    "publication_date": "2020-22-02",


    "created_at": "2020-30-03",


    "updated_at": "2020-31-03",


    "deleted_at": null


  },


  {


    "id": 4,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32157862",


    "title": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "impact_factor": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "tested_on_species": "Patient",


    "publication_date": "2020-06-03",


      "created_at": "2020-30-03",


    "updated_at": "2020-30-03",


    "deleted_at": null


  },


]

}

Endpoint details

Substances

URL: /api/substances

Substances endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

name - Name of the Substance (string)

description - Description (HTML code)

phase_of_research - Phase of research (string)

how_it_helps - How it helps (string)

drug_status - Drug status (string)

general_information - General information (HTML code)

synonyms - Synonyms (string)

marketed_as - "Marketed as" (string)

dietary_sources - Dietary sources name (string)

dietary_sources_url - Dietary sources URL (string)

prescribing_information - Prescribing information as an array of JSON objects with description and URL attributes as strings

formula - Formula (HTML code)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601 format)

deleted_at - Date when the entity was deleted in our database (Date in ISO 8601 format)

References

URL: /api/references

References endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

url - URL link of the scientific article (string)

title - Title of the scientific article (string)

impact_factor - Impact factor of the scientific article (string)

tested_on_species - What testing model was used for the study (string)

publication_date - Date of publication of the scientific article (Date in ISO 8601 format)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601

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.

Access to Data "The Transporter substrate database (TSdb) was developed to serve as a central repository of formated substrate information of transporters as well as their annotation. Most characteristic feature for our database is all the substrates are mapped to KEGG ligand compound database, thus it is easy to map all the substrate to the KEGG pathway. Our database allows you to: 1. search and browse the transporter by their substrates and organisms. 2. get an overview for all the transporter substrate in a pathway. 3. crosslink the formated substrate to other compound or metabolic pathway. 4. query the gene interaction relations for transporters. 5. discover the potential regulatory mechanisms among the transporter substrate and their inhibited metabolic enzymes. To get more diseases related with transporters such as eating disorder and IQ transporter please access our databases: IQdb, IQ associated gene resource, http://iqdb.cbi.pku.edu.cn/ EDdb, Eating disorder gene resource, http://eddb.cbi.pku.edu.cn/ "

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

Compound databases analyzed in this work.

The PubChem substance and compound databases

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.

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

Database of known biochemical compounds collected from existing biochemical databases, as well as computationally generated human phase I and phase II metabolites of known compounds.

Dataset Card for [Dataset Name]

Link to databases: https://drive.google.com/file/d/1Xjbp207zfCaBxhPgt-STB_RxwNo2TIW2/view

  Dataset Summary

The Russian version of the Spider - Yale Semantic Parsing and Text-to-SQL Dataset. Major changings:

Adding (not replacing) new Russian language values in DB tables. Table and DB names remain the original. Localization of natural language questions into Russian. All DB values replaced by new. Changing in SQL-queries filters. Filling… See the full description on the dataset page: https://huggingface.co/datasets/composite/pauq.