The Worker Adjustment and Retraining Notification (WARN) act requires companies with 50 or more employees to notify affected workers 60 days prior to closures and layoffs. WARN data includes the name of the employer, business location, number of affected workers, type (layoff or closure) and effective date of layoff or closure.

EWA-DB is a speech database that contains data from 3 clinical groups: Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and a control group of healthy subjects. Speech samples of each clinical group were obtained using the EWA smartphone application, which contains 4 different language tasks: sustained vowel phonation, diadochokinesis, object and action naming (30 objects and 30 actions), picture description (two single pictures and three complex pictures).The total number of speakers in the database is 1649. Of these, there are 87 people with Alzheimer's disease, 175 people with Parkinson's disease, 62 people with mild cognitive impairment, 2 people with a mixed diagnosis of Alzheimer's + Parkinson's disease and 1323 healthy controls.For speakers who provided written consent (total number of 1003 speakers), we publish audio recordings in WAV format. We are also attaching a JSON file with ASR transcription, if available manual annotation (available for 965 speakers) and additional information about the speaker. For speakers who did not give their consent to publish the recording, only the JSON file is provided. ASR transcription is provided for all 1649 speakers. All 1649 speakers gave their consent to the provider to process their audio recordings. Therefore, it is possible for third party researchers to carry out their experiments also on the unpublished audio recordings through cooperation with the provider.

Provenance database (Neo4j 4.1) dumps of the following Corona-Warn-App repositories:

cwa-app-android

cwa-app-ios

cwa-server

cwa-documentation

Username: covid

Password: covid19

The dataset catalogs and describes existing online, federally supported databases and tools dealing with various aspects of a potential national Early Detection and Rapid Response (EDRR) invasive species framework. This dataset is supplementary material 2 and 3 to the manuscript, "Envisioning a national invasive species information framework" (Reaser et al., 2020) published as part of a special open source issue dealing with invasive species early detection and rapid response by the journal Biological Invasions, Volume 22, Issue 1, January 2020 (https://link.springer.com/journal/10530/22/1). A user-friendly version of this dataset is also available online as a USGS Library Guide, here: https://libraryguides.usgs.gov/edrrinvasive

The State of Iowa requires any person manufacturing, storing, handling, transporting, or disposing of a hazardous substance to notify the Department of Natural Resources and local law enforcement of the occurrence of a hazardous condition. Additionally, the State of Iowa requires a person storing, handling, transporting, or land-applying manure from a confinement feeding operation or storing, handling, transporting, or land-applying manure, process wastewater, open feedlot effluent, settled open feedlot effluent or settleable solids from an open feedlot operation who becomes aware of a release to notify the Department of Natural Resources. This online database allows the public to view reported spill data in their communities.

BOLD CO1 databases reformatted to use in NanoClass (https://github.com/ejongepier/NanoClass; version 0.3.0-beta or higher) and QIIME2. Three separate databases are included for use in combination with primers mtD, LCO-HCO and CI. Databases include reference sequences and reference taxonomies for the use in NanoClass, as well as pre-trained classifiers for use in QIIME2. See usage instructions below.

For questions, please contact e.jongepier@uva.nl.

==========================================

WARNING

Please note this version of a custom BOLD CO1 db comes with absolutely no warranties.

When using this db in NanoClass, mind that it has only been tested with methods: ["megablast","minimap","spingo"] NanoClass cannot be run in combination with these BOLD CO1 databases using methods ["mothur","centrifuge","kraken"]. Compatibility with ["blast","dcmegablast","qiime","rdp"] is untested. Just remove the tools you want to skip from the NanoClass/config.yaml (see also the NanoClass documentation here: https://ejongepier.github.io/NanoClass/)

Never use this data base in combination with the NanoClass snakemake -F parameter or this BOLD CO1 database will be overwriten by the default 16S SILVA database.

==========================================

DESCRIPTION

BOLD CO1 database (last) downloaded on 20210420 and reformatted for use in QIIME2 and NanoClass. To clean-up BOLD CO1 db these steps were taken (step 7 to 11 were repeated for each of the 3 primers): - remove identical duplicates [3597874] - drop seqs with non-IUPAC characters [3597839] - remove leading and trailing ambiguous bases [3597839] - remove low quality reads - remove reads with homopolymer runs - filter by length - extract fragments between primer sequences [mtD:112450; CI:121391; LCO-HCO:65307] - dereplicate / cluster [mtD:55075; CI:46470; LCO-HCO:24835] - remove uninformative taxonomic labels [mtD:55073; CI:46466; LCO-HCO:24832] - reformat db for use in NanoClass - train classifier based on fragments

==========================================

HOW TO USE THESE DBS

Use in NanoClass:

Unzip the database and copy the reference taxonomy and (unzipped) reference sequences to the NanoClass/db/common directory, like so:

$ cp mtD/bold-v20210421-taxonomy-mtD.tsv /path/to/NanoClass/db/common/ref-taxonomy.txt $ gzip -d -c mtD/bold-v20210421-frags-mtD.fa.gz > /path/to/NanoClass/db/common/ref-seqs.fna

Something similar can be done for the other two primers (CI or LCO-HCO). Only these three primers are supported at this point.

Next, create an (empty) ref-seqs.aln file just to prevent NanoClass from automatically downloading the default 16S SILVA database, which would overwrite the BOLD db you just copied into NanoClass/db/common.

$ touch /path/to/NanoClass/db/common/ref-seqs.aln

Finally, you need to make a change to the NanoClass/Snakefile (i.e change first line into the second).

optrules.extend(["plots/precision.pdf"] if len(config["methods"]) > 2 else []) optrules.extend(["plots/precision.pdf"] if len(config["methods"]) > 200 else [])

This will disable the computation of precision plots by NanoClass as this is not supported in combination with the custom BOLD CO1 databases.

Also mind that you need to change the nanofilt minlen and maxlen in the NanoClass/config.yaml to capture the appropriate fragment length for your primer. For the mtD primer I used minlen 600 and maxlen 900 for testing.

Use in QIIME2:

You can use the trained classifier directly in QIIME2, like so:

$ qiime feature-classifier classify-sklearn
--i-classifier mtD/bold-v20210421-classifier-mtD.qza
--i-reads .qza
--o-classification .qza
--verbose

Something similar can be done for the other two primers (CI or LCO-HCO). Only these three primers are supported at this point. The classifiers have only been tested with with the sklearn algorithm.

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.


   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

There is an emerging need for comparable data for multi-microphone processing, particularly in acoustic sensor networks. However, commonly available databases are often limited in the spatial diversity of the microphones or only allow for particular signal processing tasks. In this paper, we present a database of acoustic impulse responses and recordings for a binaural hearing aid setup, 36 spatially distributed microphones spanning a uniform grid of (5x5) m^2 and 12 source positions. This database can be used for a variety of signal processing tasks, such as (multi-microphone) noise reduction, source localization, and dereverberation, as the measurements were performed using the same setup for three different reverberation conditions (T_60≈{310, 510, 1300} ms). The usability of the database is demonstrated for a noise reduction task using a minimum variance distortionless response beamformer based on relative transfer functions, exploiting the availability of spatially distributed microphones.

An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using MATLAB:
dataStruct = loadRIR('low',60,1,

An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using Python:
dataloader = wrapper.BRUDEXDataloader()#

Caution: We noticed some problems with the download of the databse when using the command line (e.g., via the zenodo_get, wget, or curl commands). These problems don't seem to appear when downloading the files with the "Download" buttons on the website instead.

Caution 2: When processing microphone signals, which are recorded with microphones that are placed *behind* loudspeakers, one can expect direct-path problems.

Caution 3: For the recordings of the noise signals, four loudspeakers were placed at about 170 cm from (and facing) the corners of the room. That is why the noise is approximately spatially diffuse only in the vicinity of the center of the room and rather spatially coherent in the corners of the room.

Caution 4: Oppposed to Zenodos information, the database does not contain 3 TB of data but about 200 GB.

Reference:

D. Fejgin, W. Middelberg, and S. Doclo,
“BRUDEX database: Binaural room impulse responses with uniformly distributed external microphones,”
in Proc. ITG Conference on Speech Communication, Aachen, Germany, Sep. 2023, pp. 1–5.

@InProceedings{Fejgin2023,
author = {D. {Fejgin} and W. {Middelberg} and S. {Doclo}},
booktitle = {Proc. ITG Conference on Speech Communication},
title = {{BRUDEX} Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones},
pages = {1-5},
month = {Sep.},
year = {2023},
address = {Aachen, Germany}
}

Database Performance Monitoring Solution Market Outlook

The global database performance monitoring solution market size was valued at $2.1 billion in 2023 and is projected to reach $4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.5% during the forecast period. The market's growth is driven by the increasing reliance on data-driven decision-making and the need for businesses to ensure optimal database performance to maintain competitive advantage.

One of the key growth drivers of the database performance monitoring solution market is the exponential increase in data generation across various industries. With the advent of big data analytics, IoT devices, and cloud computing, the volume, variety, and velocity of data have grown significantly. Organizations are increasingly adopting database performance monitoring solutions to manage, analyze, and optimize this data, ensuring that their databases operate efficiently and effectively. This trend is particularly pronounced in sectors like finance, healthcare, and retail, where data accuracy and speed are critical for business operations.

Another significant factor contributing to the market's growth is the rising complexity of database environments. Modern databases are no longer simple, standalone systems but are often part of a larger ecosystem that includes multiple data sources, cloud services, and applications. This complexity makes it challenging to maintain database performance without specialized tools. Database performance monitoring solutions provide real-time insights, alert systems, and automated optimization features that help IT teams manage these complex environments more effectively. As companies continue to adopt more advanced and intricate database technologies, the demand for robust performance monitoring tools is expected to grow.

The increasing focus on regulatory compliance and data security is also driving the market for database performance monitoring solutions. Regulations such as GDPR in Europe and HIPAA in the United States mandate strict data protection and privacy measures. Non-compliance can result in significant fines and reputational damage. Database performance monitoring solutions help organizations comply with these regulations by providing detailed logs, performance metrics, and security features that ensure data integrity and availability. As regulatory landscapes become more stringent, the adoption of these solutions is likely to increase.

In the realm of financial services, Transaction Monitoring has become an indispensable component of database performance monitoring solutions. As financial institutions handle vast amounts of transactional data daily, ensuring the integrity and performance of these databases is crucial. Transaction Monitoring systems are designed to track and analyze transactions in real-time, identifying potential fraudulent activities and ensuring compliance with regulatory standards. This capability not only helps in maintaining the security and reliability of financial operations but also enhances customer trust by safeguarding sensitive financial data. As the financial sector continues to evolve with digital banking and fintech innovations, the integration of robust Transaction Monitoring within database performance solutions is becoming increasingly vital.

Geographically, North America holds the largest market share in the database performance monitoring solution market, driven by the high adoption rate of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to rapid digital transformation, increasing investments in IT infrastructure, and a growing number of small and medium enterprises (SMEs) adopting advanced database solutions. Europe, Latin America, and the Middle East & Africa are also significant markets, each contributing to the overall market growth with their unique regional dynamics and industry requirements.

Component Analysis

The database performance monitoring solution market can be segmented by component into software and services. The software segment dominates the market due to the high demand for advanced tools and platforms that offer real-time database monitoring, predictive analytics, and automated optimization. These software solutions are designed to cater to various database environments, including SQL, NoSQL, and cloud databases, pr

Domains Database encompasses over 60 million domains with relevant traffic, ranking data, and WHOIS records for each domain. All the fields description may be found here: https://docs.dataforseo.com/v3/databases/domains/?bash.

Get expired domains, before they expire. Monitor the expiration dates and stay alert when the domain becomes available for purchase. DataForSEO Domains Database makes it a lot easier by providing records of the domains’ registration, renewal, and expiration dates.

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets. Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary. The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.

Ukrainian Plant Trait Database (UkrTrait v. 1.0) represents a collection of plant trait data from Ukraine. We compiled and digitized plant traits from local Ukrainian literature sources. Furthermore, we performed our own field and laboratory measurements of various plant traits that were not previously available in the literature. In the current version of the UkrTrait, we focus on vascular plant species that are absent from the other European trait databases, with emphasis on species that are representative for the steppe vegetation. Traits assembled from literature include life span (annuals, biennials, perennials), plant height, flowering period (flowering months), life form (by Raunkiaer), plant growth form, and others. Our own measured traits include seed mass, seed shape, leaf area, leaf nitrogen concentration, and leaf phosphorus concentration. The current version, i.e. UkrTrait v. 1.0, comprises digitized literature data of 287,948 records of 75 traits for 6,198 taxa and our own trait measurements of 2,390 records of 12 traits for 388 taxa.

More detailed information on content and methodology is available in:

Vynokurov D, Borovyk D, Chusova O, Davydova A, Davydov D, Danihelka J, Dembicz I, Iemelianova S, Kolomiiets G, Moysiyenko I, Shapoval V, Shynder O, Skobel N, Buzhdygan O, Kuzemko A (2024) Ukrainian Plant Trait Database: UkrTrait v. 1.0. Biodiversity Data Journal 12: e118128. https://doi.org/10.3897/BDJ.12.e118128

Taxonomical background. We used the Ukrainian Checklist (Mosyakin and Fedoronchuk 1999) as a primary taxonomical source to preserve the original taxa names and their corresponding trait values, which is especially meaningful for the traits collected from the existing literature sources. Additionally, we provided the crosswalks between the Ukrainian checklist and international sources: GBIF Backbone Taxonomy, World Checklist of Vascular Plants (World Checklist of Vascular Plants (World Checklist of Vascular Plants (WCVP), World Flora Online (WFO) and Euro+Med PlantBase. However, the provided nomenclature crosswalks should be used with caution, since online databases are constantly updated. Therefore, we recommend conducting an additional match of the original taxa names to obtain up-to-date nomenclature information.

The dataset includes four files in two formats (*.tsv and *.csv):

1. database-measured-traits is a dataset of the measured plant traits including generative plant height measured in the field (cm); vegetative plant height measured in the field (cm); average dry leaf mass (mg); average specific leaf area (SLA) (mm2 * mg-1); leaf nitrogen concentration (mg * g-1); leaf phosphorus concentration (mg * g-1); length of a seed (mm); width of a seed (mm); thickness of a seed (mm); variance of its three dimensions (unitless, raging from 0 to 1); average dry mass of a seed (mg).

2. database-literature-traits is a dataset including plant traits compiled from the literature sources. It includes the following traits: Average height of the whole plant; Plant flowering period; Plant life span (annuals, biennials or short-lived or perennials); Raunkiaer life form (phanerophytes, chamaephytes, hemicryptophytes, geophytes, hydrophytes, therophytes); Plant growth form (trees, shrubs, semishrubs, polycarpic and monocarpic herbs, epiphytes, woody and herbaceous lianas); Species geographic range; Leaf phenology; Rosette plants; Root system; Golubev life form (trees, shrubs, low shrubs, subshrubs, low subshrubs, polycarpic herbs, perenial monocarpic herbs, spring annuals, autumn annuals, epihydrophytes, idiohydrophytes, lianas, sparse cushion-shaped plants, spherical-shaped plants, creeping, succulents and fleshy plants, parasitic, semi-parasitic, saprophytic, carnivorous, rhizomatous, bulbosous); Biomorphological adaptations for vegetative renewal and reproduction; Listed in the Red Data Book of Ukraine (edition 2021); Conservation status according to the Red Data Book of Ukraine (edition 2021); Residence time status of alien species; Region of origin of the alien species; Naturalisation degree of the alien species; Cultivated plants; Escaped from cultivation plants.

3. traits-ontologies is a file linking trait terminology used in the UkrTrait to the Thesaurus of Plant Characteristics (TOP), the Plant Trait Ontology (TO) and the TRY Plant Trait Database.

4. taxonomy_UkrTrait is a file containing taxonomical crosswalks between the UkrTrait species list and other nomenclature sources (Mosyakin et Fedoronchuk 1999; Euro+Med PlantBase; GBIF Backbone Taxonomy; World Checklist of Vascular Plants (WCVP); World Flora Online (WFO).

Recommended citation for this database:

Vynokurov D, Borovyk D, Chusova O, Davydova A, Davydov D, Danihelka J, Dembicz I, Iemelianova S, Kolomiiets G, Moysiyenko I, Shapoval V, Shynder O, Skobel N, Buzhdygan O, Kuzemko A (2024) Ukrainian Plant Trait Database: UkrTrait v. 1.0. Biodiversity Data Journal 12: e118128. https://doi.org/10.3897/BDJ.12.e118128

THIS DATA ASSET NO LONGER ACTIVE: This is metadata documentation for the Region 7 Drycleaner Database (R7DryClnDB) which tracks all Region7 drycleaners who notify Region 7 subject to Maximum Achievable Control Technologiy (MACT) standards. The Air and Waste Management Division is the primary managing entity for this database. This work falls under objectives for EPA's 2003-2008 Strategic Plan (Goal 4) for Healthy Communities & Ecosystems, which are to reduce chemical and/or pesticide risks at facilities.

The Kidney Development Database was created to collect in one place the data from a large number of developmental studies that have a bearing on the study of kidney development. With its oldest parts dating back to 1993/4, it is, as far as we know, the earliest computer database in the field of vertebrate organogenesis. Data are displayed in tables, arranged according to a standard scheme of kidney development explained in the key. Many of the entries are derived from low-power in situs or published text-only descriptions, and should therefore be interpreted with mild caution.

Regio Baltimaade teede ja navigatsiooniinfo andmebaasis sisalduvate andmetega on võimalik lahendada logistilisi ülesandeid, lisaks sisaldab andmebaas põhjalikku navigatsiooniinfot.

Teede andmebaas hõlmab endas teede ja tänavate võrgustikku (telgjooni) koos atribuutidega: tähtsusklass, teekate, laius, asulanimi, tänavanimi, maanteenumber, E-maantee number, sõidukiirus, tee eritüüp (ramp, peale- või mahasõit, ringtee, tagasipööre, eraldatud sõidusuund, ülekäigurada, lennurada, parkla tee), suunalisus, sõiduki piirangu mask, tee struktuur (sild, tunnel, trepp, praamiliin), teede tasandilisus, sõiduradade arv, ristmikupunkt. Lisaks teedevõrgustikule on andmebaasis ka raudteed ja praamiliinid. Regio navigatsiooniinfo andmebaas sisaldab sõiduradade ja nende ühenduvuse, keskmise sõidukiiruse, pöördepiirangute, hargnevuste, ajaliste piirangute, sõiduki tüübist sõltuvate piirangute, massipiirangute, kõrguspiirangute, hoiatusmärkide, suunaviitade jne infot. Regio pakub navigatsiooni- ja logistikaülesannete lahendamiseks mitmesuguseid andmekomplekte Eestis, Lätis ja Leedus.

Regio owns and maintains Baltic road network and navigation database which is unique and the most up-to-date. Data is validated for use in navigation systems, routing and route optimization services. Regio provides various database extracts to solve navigation and logistics tasks in Estonia, Latvia and Lithuania.

Road network database contains road centrelines with attributes: road class, pavement, width, administrative unit name, street name, national road number, international E-road number, speed, road type (slip road, ramp, turn channel, roundabout, back turn, dual carriageway, crosswalk, airplane runway, car park road), direction of traffic, vehicle type applicability, road structure (bridge, tunnel, stairs, ferry), physical level of the road segment, number of traffic lanes, road node. In addition to road network, the database also includes railways and ferry lines. The navigation dataset contains information of lanes and lane connectivity, feasible average driving speed, turn restrictions, bifurcations, time restrictions, restrictions depending on vehicle type, weight restrictions, height restrictions, warning signs, road signs, etc. In addition to navigation datasets, Regio offers distance matrices, datasets of address ranges, table of street intersections or any other extracts from the road and navigation database as required.

For each personal injury accident (i.e. an accident on a road open to public traffic, involving at least one vehicle and involving at least one victim requiring treatment), information describing the accident is seized by the police unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These seizures are collected in a sheet entitled ‘Injury Analysis Bulletin’. All these forms constitute the national register of road traffic injuries, known as the ‘BAAC file’, administered by the National Interministerial Observatory for Road Safety (ONISR). The databases, extracted from the BAAC file, list all road traffic injuries occurring during a specific year in mainland France, in the overseas departments (Guadeloupe, French Guiana, Martinique, Réunion and Mayotte since 2012) and in the other overseas territories (Saint-Pierre-et-Miquelon, Saint-Barthélemy, Saint-Martin, Wallis and Futuna, French Polynesia and New Caledonia; available only from 2019 in open data) with a simplified description. This includes information on the location of the accident, as provided, as well as information on the characteristics of the accident and its location, the vehicles involved and their victims. Compared to the aggregated databases 2005-2010 and 2006-2011 currently available on the website www.data.gouv.fr, the databases from 2005 to 2022 are now annual and composed of 4 files (Characteristics – Locations – Vehicles – Users) in csv format. However, those databases conceal certain specific data relating to users and vehicles and their conduct in so far as disclosure of that data would undermine the protection of the privacy of easily identifiable natural persons or reveal the conduct of such persons, whereas disclosure of that conduct could be detrimental to them (CADA opinion – 2 January 2012). Warning: Data on the classification of injured persons hospitalised since 2018 cannot be compared to previous years following changes in the seizure process of the police. The indicator ‘injured hospitalised’ has no longer been labelled by the public statistics authority since 2019. The validity of the statistical operations that can be made from this database depends on the verification methods specific to the field of application of road safety and in particular on a precise knowledge of the definitions relating to each variable used. For any operation, it is important to take note in particular of the structure of the attached BAAC sheet and the guide to using the codification of the road traffic accident analysis bulletin. It should be noted that a number of indicators from this database are labelled by the public statistics authority (Order of 27 November 2019). The list is available at: https://www.onisr.securite-routiere.gouv.fr/statistical tools/labelled indicators

21st Century datasets utilized in evaluating NM/R-associated COVID-19 rebound [44–48].

The data in this repository is the result of vetting 16S sequences from the Genome Taxonomy Database (GTDB) release R10RS226 (r226) (https://gtdb.ecogenomic.org/; Parks et al. 2018) with the Sativa program (Kozlov et al. 2016) using the sbdi-phylomarkercheck Nextflow pipeline.Using Sativa [Kozlov et al. 2016], 16S sequences from GTDB were checked so that their phylogenetic signal is consistent with their taxonomy.Before calling Sativa, sequences longer than 2000 nucleotides or containing Ns were removed, and the reverse complement of each is calculated. Subsequently, sequences were aligned with HMMER [Eddy 2011] using the Barrnap [https://github.com/tseemann/barrnap] archaeal and bacterial 16S profiles respectively, and sequences containing more than 10% gaps were removed. The remaining sequences were analyzed with Sativa, and sequences that were not phylogenetically consistent with their taxonomy were removed.Files for the DADA2 (Callahan et al. 2016) methods assignTaxonomy and addSpecies are available, in three different versions each. The assignTaxonomy files contain taxonomy for domain, phylum, class, order, family, genus and species. (Note that it has been proposed that species assignment for short 16S sequences require 100% identity (Edgar 2018), so use species assignments from assignTaxonomy with caution.) The versions differ in the maximum number of genomes that we included per species: 1, 5 or 20, indicated by "1genome", "5genomes" and "20genomes" in the file names respectively. Using the version with 20 genomes per species should increase the chances to identify an exactly matching sequence by the addSpecies algorithm, while using a file with many genomes per species could potentially give biases in the taxonomic annotations at higher levels by assignTaxonomy. Our recommendation is hence to use the "1genome" files for assignTaxonomy and "20genomes" for addSpecies.The fasta files are gzipped fasta files with 16S sequences, the assignTaxonomy associated with taxonomy hierarchies from domain to species whereas the addSpecies file have sequence identities and species names. There is also a fasta files with the original GTDB sequence names: sbdi-gtdb-sativa.r09rs220.20genomes.fna.gz.Taxonomical annotation of 16S amplicons using this data is available as an optional argument to the nf-core/ampliseq Nextflow workflow: --dada_ref_taxonomy sbdi-gtdb (https://nf-co.re/ampliseq; Straub et al. 2020).In addition to the fasta files, the workflow outputs phylogenetic trees by optimizing branch-lengths of the original phylogenomic GTDB trees based on a 16S sequence alignment. As not all species in GTDB will have correct 16S sequences, the GTDB trees are first subset to contain only species for which the species representative genome has a correct 16S sequence. Subsequently, branch lengths for the tree are optimized based on the original alignment of 16S sequences using IQTREE [Nguyen et al. 2015] with a GTR+F+I+G4 model. The alignment files end with .alnfna, the taxonomy files with .taxonomy.tsv and the tree files (newick-formatted) end with .brlenopt.newick. They will be made available in nf-core/ampliseq for phylogenetic placement.The data will be updated circa yearly, after the GTDB database is updated.Version historyv10 (2025-04-30): Update versions in this textv9 (2025-04-29): Update to GTDB R10-RS226v8 (2025-02-18): Remove extra sequences from e.g. "1genome" files that appeared due to ties.v7 (2024-06-25): Update to GTDB R09-RS220 from R08-RS214.v6 (2024-04-24): Replace manual procedure with Nextflow pipeline. Update to GTDB R08-RS214 from R07-RS207.v5 (2022-10-07): Add missing fasta file with original GTDB names.v4 (2022-08-31): Update to GTDB R07-RS207 from R06-RS202AcknowledgementsThe computations were enabled by resources in project [NAISS 2023/22-601, SNIC 2022/22-500 and SNIC 2021/22-263] provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at UPPMAX, funded by the Swedish Research Council through grant agreement no. 2022-06725.Computations were also enabled by resources provided by Dr. Maria Vila-Costa, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona.

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NOTE: DUE TO ERRORS FOUND IN FORESEE v2.0 THE DATASET WILL BE REPLACED IN THE NEAR FUTURE [ONLY OBSERVATIONS FOR THE PAST]. PLEASE WAIT FOR FORESEE V2.1 OR ACCESS V2.1 FROM THE WEBSITE OF THE FORESEE AT http://nimbus.elte.hu/FORESEE

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The FORESEE database (Open Database FOR ClimatE Change-Related Impact Sudies in CEntral Europe) is a sophisticated, open access meteorological database that covers the 1951-2100 time period and contains observed and projected daily maximum/minimum temperature and precipitation fields for Central Europe.

For the 1951-2013 period FORESEE provides interpolated meteorological fields based on observations, and for the 2014-2100 period 10 bias corrected regional climate model (RCM) results are available based on the climate projections created and disseminated within the frame of the ENSEMBLES FP6 project using the A1B emission scenario.

More information: http://nimbus.elte.hu/FORESEE

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 25, 2015. Open content cheminformatics database linking physiology with pharmacology, it targets the action and use of pharmacological compounds in modifying protein function, while revealing molecular relationships and linking out to related databases and sites. Pharmabase has been developed as a research tool, a resource for students, and an ongoing interactive forum on the use of pharmacological compounds in cellular research. It has several navigational routes, including a graphics browser (shows graphics of cell types and pathways) and membrane transport, which also illustrates the diversity of mechanisms that are covered. Users have access to detailed compound records with interactive features, and a form to send comments to the editor. Investigators are encouraged to alert the editors to mistakes, omissions or new compound information available from their reading and research.