The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States.

The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page.

For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below.

No filters have been applied to the raw WRF output.

Occurrence dataset: A large dataset of fossil mammal occurrence data for the Quaternary (Pleistocene and Holocene) of Europe. Occurrence data comprises species or genus name, specimen information where possible, geological unit specimen was found in, age (range) of specimen and/or geological unit and any other relevant information. Data taken from multiple sources, including the Palaeobiology Database (PBDB), an open-access community dataset of global fossil occurrences (and some trait data) for all time periods and taxonomic groups. Our dataset used only the mammal records from our study region and time period. Data was taken from the NOW (New and Old Worlds) Database of fossil mammals (NOW database), another open-access community dataset. This database contains only mammal occurrence and trait data for fossil mammals throughout geological history and across the world. All additional occurrence data was collected first hand from the literature.

Trait dataset: Trait data for species in the occurrence dataset. Including (but not limited to) body size data, collected as lower first molar length and width).

This dataset is part of the DOB NOW Electrical Permit Data Collection: https://data.cityofnewyork.us/browse?Data-Collection_Data-Collection=DOB+NOW+Electrical+Permits+Data This dataset contains details of the electrical scope of work. For each Job Filing Number, there can be multiple rows/records in this dataset. For example, there might be electrical work being performed in the basement as well as on the 4th floor. One row/record for Basement and one for the floor. The job might have some 101 to 200 amps Service Switches as well as some Up to 100 amps Service Switches, and there would be one row/record for each.

A database for the entire Markarian (First Byurakan Spectral Sky Survey or FBS) Catalog is presented that combines extensive new measurements of their optical parameters with a literature and database search. The measurements were made using images extracted from the STScI Digitized Sky Survey (DSS) of F_pg (red) and J_pg (blue) band photographic sky survey plates obtained by the Palomar and UK Schmidt telescopes. The authors provide accurate coordinates, morphological type, spectral and activity classes, red and blue apparent magnitudes, apparent diameters, axial ratios, and position angles, as well as number counts of neighboring objects in a circle of radius 50 kpc. Special attention was paid to the individual descriptions of the galaxies in the original Markarian lists, which clarified many cases of misidentifications of the objects, particularly among interacting systems, larger galaxies with knots of star formation, possible stars, and cases of stars projected on galaxies. The total number of individual Markarian objects in the database is now 1544. The authors also have included redshifts which are now available for 1524 of the objectswith UV-excess radiation, as well as Galactic color excess E(B-V) values and their 2MASS or DENIS infrared magnitudes. The table also includes extensive notes that summarize information about the membership of Markarian galaxies in different systems of galaxies and about new and revised activity classes and redshifts. The new optical information on Markarian galaxies was obtained from images extracted from the STScI Digitized Sky Survey (DSS) of F_pg (red) and J_pg (blue) band photographic sky survey plates obtained by the Palomar and UK Schmidt telescopes. This table was created by the HEASARC in November 2009 based on the electronic version of the optical database of Markarian galaxies which was obtained from the CDS (their catalog J/ApJS/170/33 file table1.dat). This is a service provided by NASA HEASARC .

Appendix 11.1. List of localities to: Aiglstorfer et al. (2023): “Musk Deer on the Run – Dispersal of Miocene Moschidae in the Context of Environmental Changes” in "Evolution of Cenozoic Land Mammal Faunas and Ecosystems - 25 Years of the NOW Database of Fossil Mammals" Ed. by Casanovas-Vilar et al. Springer: Cham, Switzerland

Mammals influence nearly all aspects of energy flow and habitat structure in modern terrestrial ecosystems. However, anthropogenic effects likely have altered mammalian community structure, raising the question of how past perturbations have done so. We use functional diversity to describe how the structure of North American mammal communities changes over the past 66 Ma, an interval spanning the rebound radiation following the K/Pg and several subsequent environmental disruptions including the PETM, the expansion of grassland, and the onset of Pleistocene glaciation. For 264 fossil communities, we examine three aspects of ecological function: functional evenness, functional richness, and functional divergence. Shifts in functional diversity are significantly related to major ecological and environmental transitions. All three measures of functional diversity increase immediately following the extinction of the non-avian dinosaurs, suggesting that high degrees of ecological disturbance ..., Mammal occurrences were collected using the primary literature and paleobiology database. Mammal trait data was gathered from primary literature and various online databases including the paleobiology database and NOW database. All traits and occurrrences have a citation. All locality data information is available in a csv file. Dates were refined using the primary literature and all citations are provided in the document. I have the database attached as a csv file. , , # North American Cenozoic Mammal Traits

This dataset includes information on fossil localities and mammal traits. The fossil localities and mammal occurrence data were collected from the Paleobiology Database (https://paleobiodb.org). Some locality dates were refined using the primary literature. The sources and information gathered for this process are included in the other datafile located on my Github (see link below) called "Locality_Date_Refinement".

The sources of each trait is listed as "Ref". Some may contain multiple sources. The "Other Sources" column was added when further sources were collected to infer traits of a species. The column "Alternative_Name" reflects another name the species may have been identified as in other databases where trait data was collected.

Description of the Data and file structure

CSV File Name: Datafile4_PrimaryReference_FDNA.csv

**Description of file: This file contains information downloaded from the ...

Dataset S1 • Fossil ruminants occurrencesInformation of ruminant species occurrences in the fossil record was compiled from the New and Old Worlds (NOW) database (Fortelius 2015) and the Paleobiology Database (Alroy 2015), both accessed in July 2014. Taxa not identified at the species level were excluded (1763 occurrences; see SI Text for their temporal distribution). Subsequently, the combined database was completed and refined with information from the literature (see SI Text) and information on synonyms provided by the NOW collaborators. This dataset contains 9234 fossil occurrences of 1246 ruminant species, spanning the last 50 myr. 9186 occurrences correspond to crown ruminants. We followed Metáis and Vislovokowa (2007) and considered crown ruminants all families except Hypertragulidae, Lophiomerycidae and Archaeomerycidae. Species belonging to the six extant families (8558 occurrences, 1100 species) represent around 88% of ruminant fossil diversity, being recorded continuously sin...

Please read the README.txt file for description of the data and code. Source files can be provided upon request at this point. NOW database used in this work was published by the NOW Community, 2022. New and Old Worlds Database of Fossil Mammals (NOW). Licensed under CC BY 4.0. Retrieved 2022-04-27 from https://nowdatabase.org/now/database/. CENOGRID climate time series used in this work and provided here for ease of replication were published in Westerhold et al., 2022, https://doi.org/10.1126/science.aba6853. We downsample the time series to match the resolution of mammalian diversity time series and provide this data in CENOGRID_interp.csv file. PanTheria database (Jones et al., 2009, https://doi.org/10.1890/08-1494.1) is also provided here for ease of replication.

.NOW Whois Database, discover comprehensive ownership details, registration dates, and more for .NOW TLD with Whois Data Center.

Finland Consumer Confidence Indicator: Own Economy Now data was reported at 8.300 % in Oct 2018. This records an increase from the previous number of 6.400 % for Sep 2018. Finland Consumer Confidence Indicator: Own Economy Now data is updated monthly, averaging 4.400 % from Oct 1995 (Median) to Oct 2018, with 277 observations. The data reached an all-time high of 9.800 % in Aug 2006 and a record low of -4.000 % in Oct 1995. Finland Consumer Confidence Indicator: Own Economy Now data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.H008: Consumer Confidence Indicator.

This dataset compiles heat flow and temperature gradient data from over 44,000 wells across the United States, along with more than 6,000 related geothermal exploration resources. Originally assembled prior to 2014 for the now-retired National Geothermal Data System (NGDS), the collection includes curated well data, scanned field notes, temperature-depth curves, publications, maps, and other supporting documents. SMU Geothermal Laboratory contributed two different nationwide heat flow databases to the project. One is based on equilibrium temperature measurements (over 14,000 sites) and the other is based on corrected bottom hole temperature (BHT) data from oil and gas industry wells (over 30,000 sites). In addition, scanned field notes and temperature-depth curves were associated with approximately 6,000 specific sites in the heat flow database. Records were corrected and overlapping sites in the equilibrium heat flow database were linked between the original SMU National database and the UND Global Heat Flow database. New or related sites, which were not previously published because they lacked full heat flow content, are now included as gradient only information along with their detailed temperature data to fill in data gaps. Finally, SMU submitted over 920 scanned publications, reports, and maps suitable for full text searching. The dataset is provided in two flat-structured zip archives: one containing the curated well data and another containing related resources. An Excel index file is provided for each archive, allowing filtering by well name, location, and description. Data files are labeled with state or institutional origin where available.

The Second Byurakan Survey (SBS) is a continuation of the First Byurakan Survey (FBS), also known as the Markarian Survey. The goal of the SBS was to reach fainter objects (as faint as limiting photographic magnitudes of 19.5, about 2.5 magnitudes fainter than the Markarian survey) and discover new active and star-forming galaxies using both UV excess and emission-line techniques. In this table, a database for the entire catalog of the Second Byurakan Survey (SBS) galaxies is presented, i.e, the 1700 SBS stars listed in Stepanian (2005) are not included herein. It contains new measurements of their optical parameters and additional information taken from the literature and other databases. The measurements were made using I_pg (near-infrared), F_pg (red) and J_pg (blue) band images from photographic sky survey plates obtained by the Palomar Schmidt telescope and extracted from the STScI Digital Sky Survey (DSS). The database provides accurate coordinates, morphological type, spectral and activity classes, apparent magnitudes and diameters, axial ratios, and position angles, as well as number counts of neighboring objects in circles of radii 50 kpc around the sources. The total number of individual SBS objects in the database is now 1676. The 188 Markarian galaxies which were re-discovered by the SBS are not included in this database. the authors also include redshifts that are now available for 1576 SBS objects, as well as 2MASS infrared magnitudes for 1117 SBS galaxies. The new optical information on the SBS galaxies was obtained from images extracted from the STScI Digitized Sky Survey (DSS) of F_pg (red), J_pg (blue) and I_pg (near-infared) band photographic sky survey plates obtained by the Palomar telescope. This table was created by the HEASARC in May 2012 based on CDS Catalog J/VII/264 file sbs.dat. This is a service provided by NASA HEASARC .

The database has now been updated to include ALL mutations found in HUS patients, including those in Factor I(FI) and Membrane (MCP). Homology models are available for the domains of FI and MCP and all analysis previously available for Factor H (FH) are now also available for FI and MCP. All SNP records for FH, FI and MCP are also now included in the database on the SNP pages. Only those SNPs within coding regions will be included in the full list of mutations and within the advanced search. For more information on the different versions of the database click here. We have also redesigned the site in order to display information more clearly. Please let us know what you think of the new design. Home Information Mutations Models References Links Submit Contact Us Help Collaborators NEWS !! SEP 2009 The database has now been recovered. Please report any bugs that you notice. NEWS !! MAY 2009 We have suffered from a complete server failure this month but these issues have been sorted out and work is being carried out to restore all the data within our FH-HUS database. Sorry for any inconvenience this may have caused. NEWS !! JAN 2007 Mutations within complement Factor B have also been associated with aHUS. (Goicoechea de Jorge et al., 2007) NEW !! Nov 2006 FH-HUS Database Version 2.1 The database has now been updated to include ALL mutations found in HUS patients, including those in Factor I(FI) and Membrane (MCP). Homology models are available for the domains of FI and MCP and all analysis previously available for Factor H (FH) are now also available for FI and MCP. All SNP records for FH, FI and MCP are also now included in the database on the SNP pages. Only those SNPs within coding regions will be included in the full list of mutations and within the advanced search. For more information on the different versions of the database click here. We have also redesigned the site in order to display information more clearly. Please let us know what you think of the new design. Quick Search Enter Codon No : Choose Protein : Advanced Search Have you or someone you know been diagnosed with aHUS? The information contained on this web site is provided for scientific research purposes only. We do not give medical advice or recommend any particular treatment for specific individuals. Here are several links for patient information on aHUS: http://renux.dmed.ed.ac.uk/ http://en.wikipedia.org/ http://kidney.niddk.nih.gov http://www.webmd.com HUS HUS (Haemolytic Uraemic Syndrome) is a disease associated with microangiopathic haemolytic anemia, thrombocytopenia and acute renal failure. A subgroup of the syndrome is strongly associated with abnormalities within the complement regulator factor H gene. To read information on HUS click here. To read information on Factor H (FH) click here. FH Mutations There are currently 74 Factor H mutations, 10 Factor I mutations and 25 MCP mutations linked with HUS patients within this database. There are also 5 mutations within FH that are associated with MPGN patients. . Following HGVS guidelines, mutations are numbered starting from the ATG initiation codon and include the 18-residue signal peptide. The number of the codon with respect to the mature FH protein and consistent with the RSCB PDB entry for secreted FH (1haq.pdb) is shown alongside in parenthesis. Type I and Type II Phenotype Type I indicates that the mutant protein is either absent from the plasma or present in lower amounts. This indicates the mutation has a structural effect on the mutant protein - ie reducing the stability Type II indicates that the mutant protein is present in normal amounts in plasma. This indicates that the mutation has a functional effect on the protein ie affecting substrate binding References There are three references you can use to reference this database Saunders et al, 2007. The interactive Factor H-atypical hemolytic uremic syndrome mutation database and website: update and integration of membrane cofactor protein and Factor I mutations with structural models. Hum Mutat. 2007 28:222-234. Saunders et al, 2006. An interactive web database of factor H-associated hemolytic uremic syndrome mutations: insights into the structural consequences of disease-associated mutations. Hum Mutat. 2006 27:21-30. Saunders & Perkins, 2006. A user''s guide to the interactive Web database of factor H-associated hemolytic uremic syndrome. Semin Thromb Hemost. 2006 32:160-8. Abstract. BACKGROUND: cblC disease is a cause of hemolytic uremic syndrome (HUS), which has been primarily described in neonates and infants with severe renal and neurological lesions. PATIENTS: Two sisters aged 6 and 8.5 years presented with a latent hemolytic process characterized by undetectable or low plasma haptoglobin, respectively, associated with renal failure and gross proteinuria. Renal biopsies performed in both patients found typical findings of thrombotic microangiopathy suggesting the diagnosis of HUS. Both patients were free of neurologic signs. RESULTS: Biochemical investigations found a cobalamin processing deficiency of the cblC type. Search for additional factors susceptible to worsen endothelial damage revealed homozygosity 677C--> T mutation in the methylenetetrahydrofolate reductase gene as well as heterozygosity for a 3254T--> C mutation in factor H in the patient with the most severe clinical presentation. Long-term subcutaneous administration of hydroxocobalamin in combination with oral betaine and folic acid resulted in clinical and biological improvement in both patients. CONCLUSION: cblC disease may be a cause of chronic HUS with delayed onset in childhood. Superimposed mutation of factor H gene might influence clinical severity.

rxivist.org allows readers to sort and filter the tens of thousands of preprints posted to bioRxiv. Rxivist uses a custom web crawler to index all papers on biorxiv.org; this is a snapshot of Rxivist the production database. The version number indicates the date on which the snapshot was taken. See the included "README.md" file for instructions on how to use the "rxivist.backup" file to import data into a PostgreSQL database server. Please note this is a different repository than the one used for the Rxivist manuscript—that is in a separate Zenodo repository. You're welcome (and encouraged!) to use this data in your research, but please cite our paper, now published in eLife. Going forward, this information will also be available pre-loaded into Docker images, available at blekhmanlab/rxivist_data. Version notes: 2020-12-07*** In addition to bioRxiv preprints, the database now includes all medRxiv preprints as well. The website where a preprint was posted is now recorded in a new field in the "articles" table, called "repo". We've significantly refactored the web crawler to take advantage of developments with the bioRxiv API. The main difference is that preprints flagged as "published" by bioRxiv are no longer recorded on the same schedule that download metrics are updated: The Rxivist database should now record published DOI entries the same day bioRxiv detects them. Twitter metrics have returned, for the most part. Improvements with the Crossref Event Data API mean we can once again tally daily Twitter counts for all bioRxiv DOIs. The "crossref_daily" table remains where these are recorded, and daily numbers are now up to date. Historical daily counts have also been re-crawled to fill in the empty space that started in October 2019. There are still several gaps that are more than a week long due to missing data from Crossref. We have recorded available Crossref Twitter data for all papers with DOI numbers starting with "10.1101," which includes all medRxiv preprints. However, there appears to be almost no Twitter data available for medRxiv preprints. The download metrics for article id 72514 (DOI 10.1101/2020.01.30.927871) were found to be out of date for February 2020 and are now correct. This is notable because article 72514 is the most downloaded preprint of all time; we're still looking into why this wasn't updated after the month ended. 2020-11-18 Publication checks should be back on schedule. 2020-10-26 This snapshot fixes most of the data issues found in the previous version. Indexed papers are now up to date, and download metrics are back on schedule. The check for publication status remains behind schedule, however, and the database may not include published DOIs for papers that have been flagged on bioRxiv as "published" over the last two months. Another snapshot will be posted in the next few weeks with updated publication information. 2020-09-15 A crawler error caused this snapshot to exclude all papers posted after about August 29, with some papers having download metrics that were more out of date than usual. The "last_crawled" field is accurate. 2020-09-08 This snapshot is misconfigured and will not work without modification; it has been replaced with version 2020-09-15. 2019-12-27 Several dozen papers did not have dates associated with them; that has been fixed. Some authors have had two entries in the "authors" table for portions of 2019, one profile that was linked to their ORCID and one that was not, occasionally with almost identical "name" strings. This happened after bioRxiv began changing author names to reflect the names in the PDFs, rather than the ones manually entered into their system. These database records are mostly consolidated now, but some may remain. 2019-11-29 The Crossref Event Data API remains down; Twitter data is unavailable for dates after early October. 2019-10-31 The Crossref Event Data API is still experiencing problems; the Twitter data for October is incomplete in this snapshot. The README file has been modified to reflect changes in the process for creating your own DB snapshots if using the newly released PostgreSQL 12. 2019-10-01 The Crossref API is back online, and the "crossref_daily" table should now include up-to-date tweet information for July through September. About 40,000 authors were removed from the author table because the name had been removed from all preprints they had previously been associated with, likely because their name changed slightly on the bioRxiv website ("John Smith" to "J Smith" or "John M Smith"). The "author_emails" table was also modified to remove entries referring to the deleted authors. The web crawler is being updated to clean these orphaned entries more frequently. 2019-08-30 The Crossref Event Data API, which provides the data used to populate the table of tweet counts, has not been fully functional since early July. While we are optimistic that accurate tweet counts will be available at some point, the sparse values ...

Guided by common values, Covid Act Now is a multidisciplinary team of technologists, epidemiologists, health experts, and public policy leaders working to provide disease intelligence and data analysis on COVID in the U.S.

APIs, Visualizations and csv files of data are available for public use.

Finland CCI: Own Threat of Unemployment Now data was reported at -2.600 % in May 2019. This records a decrease from the previous number of -0.100 % for Apr 2019. Finland CCI: Own Threat of Unemployment Now data is updated monthly, averaging 0.700 % from Jan 2018 (Median) to May 2019, with 17 observations. The data reached an all-time high of 4.800 % in May 2018 and a record low of -2.600 % in May 2019. Finland CCI: Own Threat of Unemployment Now data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.H008: Consumer Confidence Indicator.

Climatic niches describe the climatic conditions in which species can persist. Shifts in climatic niches have been observed to coincide with major climatic change, suggesting that species adapt to new conditions. We test the relationship between rates of climatic niche evolution and paleo-climatic conditions through time for 71 Old-World flycatcher species (Aves: Muscicapidae). We combine niche quantification for all species with dated phylogenies to infer past changes in the rates of niche evolution rates for temperature and precipitation niches. Paleo-climatic conditions were inferred independently using two datasets: a paleo-elevation reconstruction and the mammal fossil record. We find changes in climatic niches through time, but no or weak support for a relationship between niche evolution rates and rates of paleo-climatic change for both temperature and precipitation niche and for both reconstruction methods. In contrast, the inferred relationship between climatic conditions and niche evolution rates depends on paleo-climatic reconstruction method: rates of temperature niche evolution are significantly negatively related to absolute temperatures inferred using the paleo-elevation model but not those reconstructed from the fossil record. We suggest that paleo-climatic change might be a weak driver of climatic niche evolution in birds and highlight the need for greater integration of different paleo-climate reconstructions.

Methods A full description of the data sources and processing methods is given in the manuscript.

Climatic niches:

Quantification of climatic niches (temperature and precipitation) was carried out from range maps as described in the publication. Final niche values are available here.

Paleo-climatic conditions inferred from the mammal fossil record:

Temperature and precipitation values for our study region were inferred from the mammal fossil record. Mammal fossil community data was obtained from the NOW database (accessible online http://www.helsinki.fi/science/now/.). Here we provide the data that was used in the manuscript and the climatic conditions inferred from each fossil locality.

Occurrence dataset: A relatively large (~1500) dataset of fossil mammal occurrence data for the Paleocene, Eocene and Oligocene (66 Ma - 23 Ma) of Mongolia and Northern China above 30 degrees North. Occurrence data comprises species or genus name, specimen information where possible, geological unit specimen was found in, age (range) of specimen and/or geological unit and any other relevant information. Data taken from multiple sources. The majority comes from the Palaeobiology Database (PBDB), an open-access community dataset of global fossil occurrences (and some trait data) for all time periods and taxonomic groups. Our dataset used only the mammal records from our study region and time period. A very small amount of data (10's of occurrences) was taken from the NOW (New and Old Worlds) Database of fossil mammals (NOW database), another open-access community dataset. This database contains only mammal occurrence and trait data for fossil mammals throughout geological history and across the world. Additional occurrence data (~100) was collected first hand from the literature by Dr Gemma Benevento.

Body Size dataset: Lower first molar (m1) length and width (which can be used to estimate mammal body size) was collected for approximately 60% of the individual species in the occurrence dataset (~430 species).

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems, the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The database is now (09-28-2021) also provided in R data format (.RDS) for use with the R programming language. This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://dx.doi.org/10.18434/T4H594).