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 database categorizes 91 projects using nature-based solutions (NBS) in riverine environments across the United States. These 91 projects were identified in a non-exhaustive search of Federal, State, local, and other publicly available documentation. Eight publicly available reports and websites collectively described 45 projects, while the remaining projects were sourced from individual websites or articles that described one or two projects each. For each project, we identified the following: NBS strategy or strategies implemented, total cost, year implemented, project size, and project city and state. Here, project size refers to the stream length in feet influenced by the project. For some projects, details such as project cost and project size were not recorded in publicly available documents and reports.

This dataset contains occurrence data of flora and fauna species. From the Netherlands on a 5 x 5 km scale, data from other countries are exact. Observations from Belgium are excluded and can be accessed on GBIF through Natuurpunt and Natagora. It summarizes the observations recorded by >175.000 volunteers.

Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. Global Environmental Change, 89, 102939. https://doi.org/10.1016/j.gloenvcha.2024.102939. Please also cite this paper when citing this database.

The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program.

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Observations database for reporting of nature observations. Main observed species groups are butterflies, birds, vascular plants and mammals. These groups include 88% of observations.

The aim of the Marine Nature Conservation Review (MNCR) is to extend knowledge of benthic marine habitats, communities and species in Great Britain, particularly through description of their characteristics, distribution and extent. A further aim is to identify sites and species of nature conservation importance. The still-growing MNCR database was therefore developed in-house to provide a fundamental underpinning to the work of the MNCR and Marine Conservation Branch of the JNCC. The database currently comprises a number of modules including literature reviews and field data. The field data is hierarchical with a one-to-many relationship between surveys and sites, and sites and habitats respectively. It is used to store raw field survey data from surveys of areas from all around the British coast (including the islands) by the MNCR, country agencies (e.g. English Nature) and contractors. This includes details of any photographs taken to support the survey work and cataloguing of specimens of organisms taken from the field. It also stores data from previous Nature Conservancy Council databases. It is currently being used to develop a community/biotope classification scheme. Data can be downloaded to the UK Digital Marine Atlas (UKDMA) or used in a variety of interfaced packages.

The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

Context

These data were collected and disseminated according to this publication: https://www.nature.com/articles/s41597-019-0272-6

All descriptors below are taken from this publication and are copyright of the authors.

Abstract

Thermal discomfort is one of the main triggers for occupants’ interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses.

What is Thermal Comfort and Why is it Important?

According to the widely-used definition by the American Society for Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), “Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation”. The perception of thermal indoor environment is one important driving factor for temperature change, such as adjustment of heating or cooling, or opening windows, which can be described by the adaptive principle: “If a change occurs that produces discomfort, people tend to act to restore their comfort”. Occupants have a significant influence on their indoor environment and its energy use through their presence and interactions with the building's temperature control system. Factors driving occupants-building interactions to restore thermal comfort are linked to either the intention to adjust indoor environmental parameters, or to non-environmental factors such as leaving the room. Hence, understanding thermal (dis-)comfort is crucial for appropriate design decisions and choosing suitable operation modes in buildings.

Methods

The data was collected through questionnaires distributed at least twice during two distinct seasons in Australia, China, Germany, Korea, Sweden, and United Kingdom. Respondents were university students attending lectures as they were expected to only have minor variations in age and activity level. The questionnaire consists of an introductory page, the two-page main part dealing with the scales and a fourth page addressing the respondents’ background and current thermal state. In the questionnaire scales relating to thermal sensation, thermal comfort and thermal acceptance were investigated. The first questions prompted participants to process each of these scales individually. Later questions addressed the relationship between thermal sensation and thermal comfort and thermal sensation and thermal acceptance.

Data supporting the charts in https://www.nature.com/articles/d41586-019-02479-7. Based on a snapshot of Scopus dated July 2019, across around the 7 Million author profiles that have 5 or more publications in Scopus. Information about the compilation of the dataset of which this aggregate is derived is available with the article dataset: https://data.mendeley.com/datasets/btchxktzyw/1

We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change. The recording scheme implemented at nature reserves offers unique opportunities for addressing community-level change across replicate local communities. These data have been systematically collected not as independent monitoring efforts, but using a shared and carefully standardized protocol adapted for each local community. Thus, variability in observation effort is of much less concern than in most other distributed cross-taxon phenological monitoring schemes. To enable analyses of higher-level taxonomical groups, we have included taxonomic classifications for the species in the database. The compilation of the data in a common database was initiated in the context of the project “Linking environmental change to biodiversity change: long-term and large-scale data on European boreal forest biodiversity” (EBFB), funded for 2011-2015 by the Academy of Finland, and continued with the help of other funding to OO since 2016. We organized a series of project meetings that were essential for data acquisition, digitalization and unification. These meetings were organized in Ekaterinburg (Russia) by the Institute of Plant and Animal Ecology, Ural Branch of RAS (Russian Academy of Sciences) in 2011; in Petrozavodsk (Russia) by the Forest Research Institute, at the Karelian Research Center, RAS in 2013; in Miass (Russia) by the Ilmen Nature Reserve in 2014; in Krasnoyarsk (Russia) by the Stolby Nature Reserve in 2014; in Artybash (Russia) by the Altaisky Nature Reserve in 2015; in Listvyanka, Lake Baikal (Russia) by the Zapovednoe Pribajkalje Nature Reserve in 2016; in Roztochja (Ukraine) by the Ministry of Natural Resources of Ukraine in 2016; in Puschino (Russia) by the Prioksko-Terrasnyj Nature Reserve in 2017, in Vyshinino (Russia) by the Kenozero National Park in 2018, and in St Petersburg (Russia) by the Komarov Botanical Institute of the Russian Academy of Sciences in 2019. The compilation of the data into a common database was conducted by the database coordinators (EM and CL) in Helsinki (Finland). Those participants that already held the data in digital format submitted it in the original format, and those that had the data only in paper format digitized it using Excel-based templates developed in the project meetings. Submitted data were processed by the database coordinators according to the following steps: The data were formatted so that each observation (the phenological date of a particular event in a particular locality and year) formed one row in the data table (e.g. un-pivoting tables that involved several years as the columns). The phenological event names were split into event type (e.g. “first occurrence“) and species name. The event type names (provided originally typically in Russian) were translated into English and the species names (usually provided in Russian) were identified to scientific names, using dictionaries that were partly developed and verified in the project meetings. All scientific names were periodically verified by mapping them to the Global Biodiversity Information Facility (GBIF) backbone taxonomy. We associated each data record with the following set of information fields: (1) project name, i.e. the source organization, (2) dataset name, (3) locality name, (4) unique taxon identifier, (5) scientific taxon name, and (6) event type. We imported the data records in the main database (maintained as an EarthCape database at https://ecn.ecdb.io). During the import, the taxonomic names, locality names, and dataset names were matched against already existing records.

List of Top Disciplines of NCI Nature Pathway Interaction Database sorted by citations.

GIS theme (divided into flat, line and point themes) with spatial objects from the Nature Database of the ‘Inspections’ activity programme. This group of activity types in the Nature Database consists of particular of inspections of Section 3 protected areas made by or for the municipalities and former counties and to a lesser extent the State. The dataset also includes inspections of unprotected natural areas and forests. Flat theme delimits areas covered by the individual registration in the Nature Database, often coincides with a section 3 protected area. Point theme indicates the position of the centre of a botanical documentation attached to a surface detection in the Nature Database — typically a circle with a radius of 5 m. Line theme delimits the line a record can be associated with. The line theme is only used in a small number of previous county records.

For each GIS object, the following attributes from each registry are displayed in the Nature Database: — Actidity: The activity’s unique identification number in the Nature Database. Actid is included in the URL for displaying the complete registration form for each registration. — LINK: Link to display of the complete registration form for each registration.

—Collection purposes: Indicates the purpose of the collection, e.g. “municipal inspection”.

— Responsible institution: The authority or advisory firm that is the data controller. — Field date: Date of registration in the field — Programme: The programme of activities for the registration, in this case ‘Inspection’.

— Activity: The type of activity (entry form) to which the registration belongs. — Habitat type: The habitat type that may be indicated for the individual registration. In some cases, main and sub-natural types may be indicated, which will be shown in the individual registration in the Nature Database — —Area share: The proportion of an object covered by the current registration. The same area may be linked to multiple registrations in mosaic.

— Nature status index: For many recordings, a natural state index will be calculated, cf. Nature status on terrestrial natural areas — inspections of Section 3 areas (Natural state on terrestrial natural areas — inspections of Section 3 areas (dmu.dk - https://www.dmu.dk/Pub/FR736.pdf)). The index goes from 0 (bad) to 1 (high). — Structural index: For many recordings, a structure index will have been calculated as a sub-element in the calculation of Nature State (see above.) The index ranges from 0 (bad) to 1 (high). — Species index: For many recordings, a species index will have been calculated as a sub-element in the calculation of Nature State (see above.) The index ranges from 0 (bad) to 1 (high).

— Star type: The number of species declared as “star species” on the individual registration, cf. the Nature Database.

— Star 2: The number of species declared as “2 star species” on the individual registration, cf. the Nature Database.

— Problem type: Number of species identified as “problem species” on each registration, cf. the Nature Database.

These are the data on roadkilled vertebrates from the citizen science project Roadkill (https://roadkill.at/en/) collected in the years 2014-2023, which correspond to quality level 1. Quality level 2 data are published via Zenodo and can be found here: https://zenodo.org/record/5878813#.YegX2y9Xb0o.

A description of the data collection method can be found here: https://www.nature.com/articles/s41597-022-01599-6

Every other day, data entered into the Roadkill project were validated by members of the project team to correct incorrect or inconsistent entries via the backend of the website or, if the record cannot be corrected, to delete the record. Correction of data was done (i) by the project team itself when errors were obvious (e.g., animal in the submitted image does not match the species identification listed) or (ii) by the participants themselves after being informed by the project team that a correction is needed (e.g., if the roadkill is not on a road). Since the participants collected the data during their daily routine, the submitted data are so-called presence-only data.

To ensure the quality of the data, we used a stepwise selection process that allowed us to classify the submitted data into three quality levels at the end of this process:

Quality Level 1: Records with correct species identification (either by experts or by images) and consistent data. Quality level 2: Records with consistent data, but no possible validation of the species Deleted: records with inconsistent data and no possible validation of the species

We thank all the citizen scientists who reported the data and helped identify the species. Without the voluntary work of the citizen scientists this project would not be possible.

This dataset supplements the code at https://github.com/aozalevsky/alphafold2_vs_swissmodel for the comparison of the AlphaFold2 database (https://alphafold.ebi.ac.uk) with the SwissModel Repository (https://swissmodel.expasy.org/repository). Results of the analysis were published as part of the AlphaFold community review https://www.nature.com/articles/s41594-022-00849-w

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Plant Macrofossil. The data include parameters of plant macrofossil (population abundance) with a geographic location of Iowa, United States Of America. The time period coverage is from 6889 to 1168 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Here we present a geospatial dataset representing local- and regional-scale aquifer system boundaries, defined on the basis of an extensive literature review and published in GebreEgziabher et al. (2022). Nature Communications, 13, 2129, https://www.nature.com/articles/s41467-022-29678-7

The database contains 440 polygons, each representing one study area analyzed in GebreEgziabher et al. (2022). The attribute table associated with the shapefile has two fields (column headings): (1) aquifer system title (Ocala Uplift sub-area of the broader Floridan Aquifer System), and (2) broader aquifer system title (e.g., the Floridan Aquifer System).

• to explore the available data via an interactive app please visit the following URL: https://ucsb.maps.arcgis.com/apps/instant/nearby/index.html?appid=2a238e4ed2434d21b3b53c861a064d8f
• to explore the 3D nature of a dozen aquifer systems please see the following storymap: https://storymaps.arcgis.com/stories/a91f061759e64e44af38daf6cefa4259

The Standing Waters Database is due to be decommissioned on 11th July 2025. A replacement application is being built.This involves identifying and estimating the abundance of emergent, submerged, floating leaved, and free-floating macrophytes that grow in or near the water. The results of these surveys are held in the Standing Waters Database which is available to view on the SNH website. (https://www.nature.scot/information-library-data-and-research/snhi-data-services/standing-waters-database).View the database application here: https://gateway.snh.gov.uk/pls/apex_cagdb2/f?p=111

This database represents a compilation of 13980 agronomical yields predictions for main crops worldwide including effects of climate change on them as well as adaptation strategies to mitigate it. The information was extracted from 88 documents (peer-reviewed articles, book chapters, and technical reports). These articles were used in Challinor et al., (2014), available at https://www.nature.com/articles/nclimate2153

SpliceAI RocksDB for chromosome 1 of hg19 as used in the AbSplice publication: https://www.nature.com/articles/s41588-023-01373-3

Precomputed SpliceAI scores for all SNVs and indels up to 4 nucleotides are stored in this database. To use this database for fast computation of SpliceAI predictions see: https://github.com/gagneurlab/spliceai_rocksdb

This is also implemented in the AbSplice package: https://github.com/gagneurlab/absplice

This dataset includes SpliceAI scores. The scores are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc., see the GitHub repository of SpliceAI: https://github.com/Illumina/SpliceAI/tree/master

Updated Version of a Rights of Nature Database (May 2024). The data was taken and adapted from the Eco-Jurisprudence Monitor (ecojurisprudence.org)