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

Built using OceanDNA catalog (https://www.nature.com/articles/s41597-022-01392-5) and clusters from VEBA (https://academic.oup.com/nar/article/52/14/e63/7697622)

Leviathan:

https://github.com/jolespin/leviathan

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 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

Water is essential for California’s people, economy, and environment. Centuries of water management through dams and diversion have altered the flows in many streams and rivers, which can harm the freshwater ecosystems. The Nature Conservancy and the United States Geological Survey (USGS) partnered to generate estimates of natural flows (expected streamflow in the absence of human modification) in all the streams and rivers in California from 1950-2015.

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.

Comprehensive dataset of 21 Nature preserves in Province of Como, Italy as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset containing 352 verified Nature preserve businesses in Denmark with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 941 verified Nature preserve businesses in Wisconsin, United States with complete contact information, ratings, reviews, and location 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

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.

TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the WorldFlora R package to World Flora Online or the World Checklist of Vascular Plants for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

TreeGOER can be used in combination with the CitiesGOER database (https://doi.org/10.5281/zenodo.8175429) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. TreeGOER could also be used with the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756. This high resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.

Changes between different versions of the databases are documented in a specific sheet in the metadata file.

The development of TreeGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. When using TreeGOER in your work, cite the publication (Kindt 2023) as well as this repository using the DOI (https://doi.org/10.5281/zenodo.7922927).

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.

Database created in 2006 with the help of the Department of Environment and Territorial Policy of the Basque Government. It contains information on vascular plants of the Basque Country and bordering territories referring to bibliographic citations. Currently, it has 202,412 citations corresponding to 4,970 species. In addition, 1,028 books/articles are registered. In addition to bibliographic citations, Aranzadi Science Society has another extensive record of data relating to Herbarium sheets. These correspond to another dataset, Aranzadi Zientzi Elkartea.

Context

Dataset is downloaded from https://amp.pharm.mssm.edu/archs4/download.html The methods are described in Nature Communications paper: https://www.nature.com/articles/s41467-018-03751-6

The ARCHS4 data provides user-friendly access to multiple gene expression data from the GEO database. (https://www.ncbi.nlm.nih.gov/geo/ ). While in GEO database most of data is stored in raw formats, ARCHS4 provides prepared count matrix expression data. While GEO contains data stored separately for each research paper, ARCHS4 collects all the information in one single matrix. One may consult the main site for further information.

Main data files are in H5 (HD5, Hierarchical Data Format ) file format https://en.wikipedia.org/wiki/Hierarchical_Data_Format It contains expression data, as well as annotation data and futher meta-information. There are several other auxilliary files like TSNE 3d projection (in CSV format) and correlation matrices for genes for human and mouse in feather format.

Content

The main file (for human): human_matrix.h5 - contains data matrix - which is 238522 samples times 35238 genes, as well as, various meta information: gene names, samples information (tissue, etc), references to GEO database id where all the details can be found.

There is also similar data for mouse, csv files with TSNE images, correlation matrices for genes.

Acknowledgements

The ARCHS4 project is by :

'Alexander Lachmann', 'alexander.lachmann@mssm.edu', update: '2020-02-06'

Bird specimens from Japan and other countries, deposited at Kanagawa Prefectural Museum of Natural History

Nature Environment and Pollution Technology Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Environment and Pollution - The journal was established initially by the name of the Journal of Environment and Pollution in 1994, whose name was later changed to Nature Environment and Pollution Technology in the year 2002. It has now become open access online journal from the year 2017 with ISSN: 2395-3454 (Online). The journal was established especially to promote the cause for the environment and to cater to the need for rapid dissemination of the vast scientific and technological data generated in this field. It is a part of many reputed international indexing and abstracting agencies. The Journal has evoked a highly encouraging response among the researchers, scientists, and technocrats. It has a reputed International Editorial Board and publishes peer-reviewed papers. The Journal has also been approved by UGC (India). The journal is Abstracted and Indexed by AGRIS (UN-FAO) British Library Centre for Research Libraries Chemical Abstracts, U.S.A. CSA: Environmental Sciences and Pollution Management EBSCO’s Database Electronic Social and Science Citation Index (ESSCI) Elektronische Zeitschriftenbibliothek (EZB) Elsevier Bibliographic Databses Like EI Compendex, Geobase, EnCompass, Etc. Environment Abstract, U.S.A. Geobase GetCited Google Scholar Index Copernicus International (ICI) Indian Citation Index Indian Science Indian Science Abstracts, New Delhi, India J-Gate JournalSeek NeuJour, USA Paryavaran Abstract, New Delhi, India Pollution Abstracts, U.S.A. ProQuest, U.K. Research Bible (Japan) Science Central Scopus, SJR Sherpa UDL-EDGE (Malaysia) Products like i-Journals, i-Focus and i-Future WorldCat Zetoc, Agriquest Zoological Records (Thomson Reuters) The journal publishes both original research and review papers. The ideology and scope of the Journal include the following. Monitoring, control, and management of air, water, soil and noise pollution Solid waste management Industrial hygiene and occupational health Biomedical aspects of pollution Toxicological studies Radioactive pollution and radiation effects Wastewater treatment and recycling etc. Environmental modelling Biodiversity and conservation Dynamics and behaviour of chemicals in the environment Natural resources, wildlife, forests, and wetlands, etc. Environmental laws and legal aspects Environmental economics Any other topic related to the environment

Original publication: https://www.nature.com/articles/s41893-024-01433-4

• Excel file listing all sectors, countries and extensions of the MRIO database REX3

• Species loss factors applied to the land transitions and land-use states

• Excel file containing the data for the figures and results presented in this study.

ANI-1E: An equilibrium database from the ANI-1 database v.2.0

Authors: Luis Itza Vazquez-Salazar and Markus Meuwly
E-mail contact: litzavazquezs@gmail.com and m.meuwly@unibas.ch

From the SMILES strings, provided by Smith et al., initial geometries using OpenBabel were generated.
Subsequently, geometries were optimised using PM7 implemented in MOPAC2016, before a final geometry optimisation and frequency calculation at the ωB97x/6-31G(d) level of theory performed using Gaussian09.
The final results were checked to assure that they did not contain imaginary frequencies and therefore correspond to a minimum on the potential energy surface, which can be different from the global minima for the molecule.
The total number of molecules is 57455; 7 molecules were unstable for optimisation. The format of the files is .xyz, following the style of the QM9 database and it contains the geometry minimal in energy, rotational constants, dipole moments, polarizabilities, along with energies of HOMO and LUMO, electronic spatial extent, zero-point energy, enthalpies, and free energies of atomisation. The header of the .xyz file follows the format
given in Table 3 of the QM9 paper with the difference that the TAG is 'ANI-1E'. Additionally, a file with the original smiles of the ANI-1 dataset and the smiles of ANI-1E is added. The seven molecules (56176,56177,56213,56214,56215,56216,56217) that do not converge are not included
in the new database. The .xyz of the final structures are available in the folder 'failed'. The output files for
all optimizations are available upon reasonable request to the authors.

We acknowledge Alfred Andersson and Prof. David van der Spoel for attracting our attention to
the problems on the first version of our database.

Raw data supporting the Springer Nature Data Availability Statement (DAS) analysis in the State of Open Data 2024. SOOD_2024_special_analysis_DAS_SN.xlsx contains the DAS, DOI, publication date, DAS categories and related country by Insitution of any author.SOOD 2024_DAS_analysis_sharing.xlsx contains the summary data by country and data sharing type.Utilizing the Dimensions database, we identified articles containing key DAS identifiers such as “Data Availability Statement” or “Availability of Data and Materials” within their full text. Digital Object Identifiers (DOIs) of these articles were collected and matched against Springer Nature’s XML database to extract the DAS for each article. The extracted DAS were categorized into specific sharing types using text and data matching terms. For statements indicating that data are publicly available in a repository, we matched against a predefined list of repository identifiers, names, and URLs. The DAS were classified into the following categories:1. Data are available from the author on request. 2. Data are included in the manuscript or its supplementary material. 3. Some or all of the data are publicly available, for example in a repository.4. Figure source data are included with the manuscript. 5. Data availability is not applicable.6. Data are declared as not available by the author.7. Data available online but not in a repository.These categories are non-exclusive: more than one can apply to any one article. Publications outside the 2019–2023 range and non-article publication types (e.g., book chapters) that were initially included in the Dimensions search results were excluded from the final dataset. Articles were included in the final analysis after applying the exclusion criteria. Upon processing, it was found that only 370 results were returned for Botswana across the five-year period; due to this low number, Botswana was not included in the DAS focused country-level analysis. This analysis does not assess the accuracy of the DAS in the context of each individual article. There was no manual verification of the categories applied; as a result, terms used out of context could have led to misclassification. Approximately 5% of articles remained unclassified following text and data matching due to these limitations.