This database contains specimens of vertebrates from the Gothenburg Natural History Museum, collected from the early 1800's to present. New specimens are added continuously. Most of the birds and mammals are preserved as skins, while most fishes, amphibians and reptiles are preserved in alcohol. There are however, both wet and dry preserved specimens of a wide array of taxa from the entire world. The collections also include eggs and skeletons. The collection contains in total slightly less than 63 000 specimens, of which about half are birds. Fishes, amphibians & reptiles, and mammals are represented by roughly 10 000 specimens each. The bone collection contains about 18 000 skeletons, and the egg collection approximately 10 000 broods of which more than 1000 includes egg from common cuckoo, Cuculus canorus.

Natural history specimen data linked to collectors and determiners held within, "Plant Specimen Database of Tama Forest Science Garden, Forestry and Forest Products Research Institute, Japan". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/38e8b720-9074-4471-a016-73cae18a6c1c using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/38e8b720-9074-4471-a016-73cae18a6c1c. Formatted as a Frictionless Data package.

The Collection includes the Fish, Herpetology, Bird and Mammal Collections of the Natural History Museum of Los Angeles County. They are worldwide collections with greatest concentration in North and South America, Africa, the Pacific Ocean, and the Antarctic.

This zip file contains the Code History Database for the United Kingdom as at December 2017. To download the zip file click the Download button. The Code History Database (CHD) contains the GSS nine-character codes, where allocated, for current and new statistical geographies from 1 January 2009. The codes consist of a simple alphanumeric structure; the first three characters (ANN) represent the area entity (i.e. type; or category of geography) and the following six characters (NNNNNN) represent the specific area instance. The CHD provides multiple functionality including details of codes, relationships, hierarchies and archived data. The CHD can be used in conjunction with the Register of Geographic Codes (RGC) that summarises the range of area instances within each geographic entity. The GSS Coding and Naming policy for some statistical geographies was implemented on 1 January 2011. From this date, where new codes have been allocated they should be used in all exchanges of statistics and published outputs that normally include codes. For further information on this product, please read the user guide and version notes contained within the product zip file.Updated Geographies

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New entity for County Electoral Divisions (E58)

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Updates to Electoral Wards in England (E05), Clinical Commissioning Groups in England (E38) (name changes) and Waste Authorities in England (E50).

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Updates to all Geography Constitutions to as at 31/12/2017

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Changes to the SI Details, Name Changes and Information table.

Database Changes

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Updates to form design to account for December 2017 version have been made.Please Note - Field called Combined in Equivalents.csv and in database has been removed. This field was used for QA purposes. 02/03/18. Some CCGs added to Equivalents table. (09/03/18)

Natural history specimen data linked to collectors and determiners held within, "Type Specimen Database of Museum and Institute of Zoology PAS". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/1757e800-9fcd-11da-a90d-b8a03c50a862 using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/1757e800-9fcd-11da-a90d-b8a03c50a862. Formatted as a Frictionless Data package.

Natural history specimen data linked to collectors and determiners held within, "The InBIO Barcoding Initiative Database: A taxonomic revision of the Western Palaearctic genus Cacochroa Heinemann, 1870 (Lepidoptera, Depressariidae)". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/900a387b-cf8b-479b-b983-69fa973f3bcd. Formatted as a Frictionless Data package.

This project was undertaken to establish a computerized skeletal database composed of recent forensic cases to represent the present ethnic diversity and demographic structure of the United States population. The intent was to accumulate a forensic skeletal sample large and diverse enough to reflect different socioeconomic groups of the general population from different geographical regions of the country in order to enable researchers to revise the standards being used for forensic skeletal identification. The database is composed of eight data files, comprising four categories. The primary "biographical" or "identification" files (Part 1, Demographic Data, and Part 2, Geographic and Death Data) comprise the first category of information and pertain to the positive identification of each of the 1,514 data records in the database. Information in Part 1 includes sex, ethnic group affiliation, birth date, age at death, height (living and cadaver), and weight (living and cadaver). Variables in Part 2 pertain to the nature of the remains, means and sources of identification, city and state/country born, occupation, date missing/last seen, date of discovery, date of death, time since death, cause of death, manner of death, deposit/exposure of body, area found, city, county, and state/country found, handedness, and blood type. The Medical History File (Part 3) represents the second category of information and contains data on the documented medical history of the individual. Variables in Part 3 include general comments on medical history as well as comments on congenital malformations, dental notes, bone lesions, perimortem trauma, and other comments. The third category consists of an inventory file (Part 4, Skeletal Inventory Data) in which data pertaining to the specific contents of the database are maintained. This includes the inventory of skeletal material by element and side (left and right), indicating the condition of the bone as either partial or complete. The variables in Part 4 provide a skeletal inventory of the cranium, mandible, dentition, and postcranium elements and identify the element as complete, fragmentary, or absent. If absent, four categories record why it is missing. The last part of the database is composed of three skeletal data files, covering quantitative observations of age-related changes in the skeleton (Part 5), cranial measurements (Part 6), and postcranial measurements (Part 7). Variables in Part 5 provide assessments of epiphyseal closure and cranial suture closure (left and right), rib end changes (left and right), Todd Pubic Symphysis, Suchey-Brooks Pubic Symphysis, McKern & Steward--Phases I, II, and III, Gilbert & McKern--Phases I, II, and III, auricular surface, and dorsal pubic pitting (all for left and right). Variables in Part 6 include cranial measurements (length, breadth, height) and mandibular measurements (height, thickness, diameter, breadth, length, and angle) of various skeletal elements. Part 7 provides postcranial measurements (length, diameter, breadth, circumference, and left and right, where appropriate) of the clavicle, scapula, humerus, radius, ulna, scarum, innominate, femur, tibia, fibula, and calcaneus. A small file of noted problems for a few cases is also included (Part 8).

Approximately 11,000 skins, approximately 9,000 eggs, and about 650 nests

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on June 9, 2025. A database which retains extensive clinical information about study subjects recruited by the Alzheimer's Disease Research Center Clinical Core, as well as other individuals with neurological diseases. In addition to clinical information, the database has basic demographics, medical history (including risk factors such as smoking), and a detailed family history from all subjects. Some entries have neuropsychological measures. Users can access a Summary Database which contains the most commonly requested variables. A data dictionary describing the variables in the Summary Database is available.

Our dataset collects comprehensive life-history, ecological, and biogeographic data on global snakes.

We present TRAD: Thermal traits of anurans database for the Southeastern United States, a database of thermal trait values related to physiological (critical thermal minima and maxima, preferred temperature, mass) and behavioral thermoregulation (activity period, retreat emergence temperature, basking temperature, foraging temperature minimum and maximum) for 40 anuran species found within the southeastern United States. Using a species-centric approach, we collated this database by first identifying trait values from large reservoirs of amphibian ecology and natural history and then searching the literature using primarily Web of Science to thoroughly identify available thermal trait data. The TRAD database provides a data reservoir for thermal trait data that extends the ecological trait data stored in ATraiU (Moore et al., 2020). In total, the TRAD database contains 858 reported trait values from 267 peer reviewed papers, dissertations, thesises, or rarely guides. TRAD has a 43 ...

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

Download .zipMaps and data associated with oil-and-gas wells represent one of the largest datasets at the Ohio Department of Natural Resources. This GIS data layer contains all the locatable oil-and-gas wells in Ohio. The feature is derived from coordinates obtained from the Division of Oil and Gas Resources Management (DOGRM) oil and gas well database – Risk Based Data Management System (RBDMS). The RBDMS database has a long history and is a comprehensive collection of well data from historic pre-1980 paper well records (digitized by the Division of Geological Survey (DGS)) to post-1980 DOGRM database solutions.Since 1860, it is estimated that more than 267,000 oil-and-gas wells have been drilled in Ohio. The compressed file also includes a feature used to connect the surface location to the bottom location of a well that has been drilled directionally or horizontally. This feature is NOT the actual wellbore path, it is simply a graphical representation indicating the relationship between the two well points.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Oil & Gas ResourcesOil and Gas Resources Management2045 Morse Road Bldg F-2Columbus, OH, 43229-6693Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov Data Update Frequency: Every Saturday

Milky seas are a rare form of nocturnal oceanic bioluminescence distinguished by a steady, non-flashing, white/gray/green glow. Scientific inquiry into milky seas has, for centuries, been held back by the remote ephemeral nature of this phenomenon. Combining centuries of eyewitness accounts with modern satellite observations, we present a curated list of milky sea observations since 1600. This database greatly expands the ability to study when and where milky seas occur, as well as the commonly observed features of a milky sea., , , # A curated database of milky sea observations from 1600 to present

https://doi.org/10.5061/dryad.0gb5mkmbc

Description of the data and file structure

The data in this archive was collected for the paper "From Sailors to Satellites: A Curated Database of Milky Seas Since 1600". By combining centuries of eyewitness accounts with satellite observations for the first time, we hope to expand the ability to study and understand milky seas.

Files and variables

File: Milky_Sea_Database.pdf

Description:Â A human readable PDF of every eyewitness account and satellite observation within the database. This file contains the date, location, description, and who reported the account for every milky sea observation. This is Supplemental 1 for the paper.

File: Milky_Sea_Database.tsv

Description: A machine-readable tab-separated values file containing detailed information on every milky sea observation within the database. ...,

From the original paper:

A new strand of literature aims at building the most comprehensive and accurate database of notable individuals. We collect a massive amount of data from various editions of Wikipedia and Wikidata. Using deduplication techniques over these partially overlapping sources, we cross-verify each retrieved information. For some variables, Wikipedia adds 15% more information when missing in Wikidata. We find very few errors in the part of the database that contains the most documented individuals but nontrivial error rates in the bottom of the notability distribution, due to sparse information and classification errors or ambiguity. Our strategy results in a cross-verified database of 2.29 million individuals (an elite of 1/43,000 of human being having ever lived), including a third who are not present in the English edition of Wikipedia.

Database contains specimen records from the Mollusca collection of the Naturalis Biodiversity Center (Leiden, Netherlands). The zoological specimens come from the National Museum of Natural History (RMNH; Rijksmuseum voor Natuurlijke Historie), later National Museum of Natural History, Naturalis in Leiden and the Zoological Museum Amsterdam (ZMA) and new acquisitions. This database replaces the dataset "Zoological Museum Amsterdam, University of Amsterdam (NL) – MolluscaConidae", "Zoological Museum Amsterdam, University of Amsterdam (NL) – MolluscaNetherlands", and "Zoological Museum Amsterdam, University of Amsterdam (NL) – MolluscaPectinoidea" which were hosted earlier on the GBIF dataportal and contains many new and updated records, especially from the RMNH collection. This dataset is an abstract of the Naturalis production database (BRD) of 2012-12-12. The datasets Zoological Museum Amsterdam, University of Amsterdam (NL) – MolluscaTypes, and Naturalis National Natural History Museum (NL) – Mollusca fossils, are still separately hosted at GBIF, and are not included in this dataset. The old registration numbers of the former institutions (RMNH, ZMA) are also mentioned in the dataset. The Naturalis BC collection contains over 42.000 species of Recent Mollusca. Locality data have only been added in part to this molluscan dataset. Many identifications are original and can be as old as over a century. Many species level names were recently (2011-2012) updated with the help of literature and web based taxonomic databases containing synonymies.

Open the Data Resource: https://star.chesapeakebay.net/ The Chesapeake Bay Program's Strategic Science and Research Framework (SSRF) was developed to identify and assess the partnership's short- and long-term science needs. These science needs are captured and tracked in this continually updated database. The science needs that are captured in this database were:

Identified as necessary to make progress toward a Chesapeake Bay Watershed Agreement goal or outcome, Expressed through the Chesapeake Bay Program's Strategy Review System process, and/or Listed as a recommendation within a Scientific and Technical Advisory Committee workshop report.

The Chesapeake Bay Program uses this database to engage stakeholders, identify opportunities to better align or evolve resources, update activities and workgroups to address needs, and inform STAC of its research priorities. This database can also be used by science providers to identify projects or collaborations of interest on which to engage the program. Science providers can represent a wide range of entities including, but not limited to, academic institutions, federal and state agencies, local entities, non-profit organizations and citizen science programs.

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

DATA UPDATE 4/29/25. ONLY CONTAINS DATA FROM 2/3/1940-4/29/2025

NOTE DONALD TRUMP SEPERATED INTO Donald J. Trump_1.csv and Donald J. Trump_2.csv ACCORDING TO TERM This dataset contains a database of executive orders from 1940-2025. File containing all presidents combined in all_executive_orders.csv All files have the following header: citation,document_number,pdf_url,publication_date,signing_date,title,executive_order_number,president * citation: federal register citation * document_number: federal register document number * pdf_url: pdf copy of order * publication_date: date of order publication * signing_date: date of order signing * title: order title * executive_order_number: number * president: president that signed order

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19549673%2F4e9dbd32f895bd14c22013d139f84614%2F100_days.png?generation=1745969219295176&alt=media" alt="">

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .