The Hopkins U.S. System Index (HUSSI) is an information resource for forest entomologists, systematic entomologists, pest management specialists, foresters, and students. It is a collection of notes on thousands of insect and damage specimens from forests or wood products taken mainly in the United States, with some from Canada, Mexico, Central America, South America and other regions. Specimens related to the records are in collections at several USDA Forest Service installations; at the U.S. National Museum, Smithsonian Institution, Washington, DC; and at several universities. The paper-based system, conceptualized by Dr. A.D. Hopkins in 1894 and formally initiated by the USDA in 1902, now contains over 160,000 written records. Some of these records have been digitized as follows. The database includes information on location, date, taxon, insect and plant host association; other searches, measurements, and quantitative data; and other information in tabular or narrative form. The original database file was designed for importing into dBase, Access, FoxBase, RBase, Paradox, and other XBase-type programs. The data dictionary describes information entered in the 16 fields abstracted from the Hopkins U.S. System records. Then you can structure specific queries and reports that show:

Plant hosts Insect hosts Parasites & predators Geographic distribution Collection dates and collectors Location of original written notes Location of insect or damage specimens Resources in this dataset:Resource Title: Data files rezipped October 2015. File Name: allwest2.zipResource Description: The original allwest.exe data package offered by U.S. Forest Service was opened using WinZip 15 (Windows 7) and saved as a zip archive suitable for opening with typical archive utilities on both Windows and Macintosh. Downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml.

Includes:

README.TXT : Instructions from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

TITLPAGE.TXT : Title page.

HUSINTRO.TXT : Background information on the Hopkins U.S. System and the Hopkins U.S. System Index (HUSSI).

HUSSTAT.TXT : Description of HUSSI files at each repository.

HUSREPOS.TXT : List of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.

HUSDTDIC.TXT : Data dictionary for HUSSI records.

DBDESAW2.TXT : Description of ALLWEST2 database.

ALLWEST2.DBF : HUSSI records from all western USDA Forest Service repositories (as of 1986), except PSWNB records from notebooks at the Pacific Southwest Experiment Station, Berkeley, CA. PSWNB records are in a seperate archive.Resource Title: Flat version of the HUSSI database. File Name: ALLWEST2.csvResource Description: The file ALLWEST2.DBF from ALLWEST.EXE was converted to a comma separated values file using LibreOffice 5.0.2.2. This appears to include all 37,198 records with 16 columns as described in the data dictionary. Suitable for use with most applications that can handle CSV input.Resource Title: Original text version of HUSSI data dictionary. File Name: HUSDTDIC.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original list of repositories (as of 1986) for Hopkins U.S. System records described in HUSSI.. File Name: HUSREPOS.TXTResource Description: Included in ALLWEST2 archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

Expands all the acronyms of the repositories holding physical cards represented in the database.Resource Title: Original README.TXT from the ALLWEST archive. File Name: README.TXTResource Description: Original README.TXT from the ALLWEST archive. The explanations appear in the zipped archive, and have been used as a basis for this dataset description. Includes obsolete instructions for using self-extracting archive on Windows 95 and Windows 3.x operating systems.Resource Title: Original Database Description from ALLWEST2 archive. File Name: DBDESAW2.TXTResource Description: Included in ALLWEST2 archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml. Title: Original introductory text from ALLWEST2 archive. File Name: HUSINTRO.TXTResource Description: Included in ALLWEST archive downloaded in October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original title page for HUSSI. File Name: TITLPAGE.TXTResource Description: Included in ALLWEST archive downloaded from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml Title: Original statistics file for HUSSI records . File Name: HUSSTAT.TXTResource Description: A description of record types for Hopkins U.S. System files and number of HUSSI records for each repository as of March 1991. Part of the ALLWEST2 archive downloaded October 2015 from http://www.fs.fed.us/pnw/mdr/past/bmnri/research/database/hussi.shtml

Interactive data set on lead exposure (Blood Lead Concentrations greater than or equal to 25 micrograms per deciliter) of adults in the United States. The data comes from laboratory-reported elevated blood lead levels. Recent research has led to increased concerns about the toxicity of lead at low doses. Reflecting this increased concern, the ABLES program updated its case definition for an elevated BLL to a blood lead concentration greater than or equal to 10 micrograms per deciliter in 2009. This new case definition has also been: (1) recommended by the Council of State and Territorial Epidemiologists in 2009; (2) included in CDC''s list of nationally notifiable conditions in 2010; and (3) adopted as the Healthy People 2020 Occupational Safety and Health Objective 7. Given this new case definition, NIOSH will update the ABLES Charts and Interactive Database to include lead exposures to blood lead level greater than or equal to 10 micrograms per deciliter in the near future.

A machine usable dictionary containing thousands of words, each with linguistic and psycholinguistic attributes (psychological measures are recorded for a small percentage of words). The dictionary may be of use to researchers in psychology or linguistics to develop sets of experimental stimuli, or those in artificial intelligence and computer science who require psychological and linguistic descriptions of words.

Project website: http://dhbasecamp.humanities.ucla.edu/afamfilm/about-the-project/

We are a group of undergraduate and graduate students in the Digital Humanities program at the University of California, Los Angeles. This main goal of this project was to collaboratively create a database on early African-American silent race films by drawing together information in a wide range of primary and secondary sources. For the purpose of this project, we determined that we would only include silent films created before 1930 for African-American audiences. This definition was the main factor that informed our decisions to include or exclude pieces of data. (You can read more about how we arrived at our definition here: http://dhbasecamp.humanities.ucla.edu/afamfilm/whatis/definition/.)

The database we have created contains information on films, actors, production companies, and other aspects of early silent-era African American race films. The database is intended to allow the public to learn about this period in film history that is too rarely discussed.

A web-based database of protein interaction sites. PiSITE provides not only information of interaction sites of a protein from single PDB entry, but also information of interaction sites of a protein from multiple PDB entries including similar proteins. PiSite also provides a list of sociable proteins, proteins with multiple binding states and multiple binding partners.In PiSITE, the identification of the binding sites of protein chains is performed by searching the same proteins with different binding states in PDB at first, and then mapping those binding sites onto the query proteins. The database PiSITE provides real interaction sites of proteins using the complex structures in PDB. According to the progress of several structural genomic projects, we have a large amount of structural data in PDB. Consequently, we can observe different binding states of proteins in atomic resolutions, and can analyze actual interaction sites of proteins. It will lead better understandings of protein interaction sites in near future. Usual practice to identify the interaction site has been done using a representative complex in PDB. However, for the proteins with multiple partners, non-interaction sites identified by using a single complex structure is not enough, because some part of the non-binding sites may be involved in the interaction sites with another proteins. Therefore, the real interaction sites should be obtained by using all of the binding states in PDB. For the purpose, the identifications of the binding site in PiSITE are done by searching the same proteins with different binding sites in PDB at first, and then mapping the binding sites onto the query proteins. PiSITE also provides the lists of transient hub proteins, which we call sociable proteins to clarify the different of so-called hub proteins. The sociable proteins are identified as the proteins with multiple binding states and multiple binding partners. On the other hand, so-called hub proteins have been identified as the proteins at the hub position in protein-protein interaction networks obtained by large-scale experiments, but the definition of the hub proteins cannot differentiate transient hub proteins from stable ones, although the differentiation is critically important for the better understanding of protein interaction networks. In addition, the usual definition of hub proteins can contain supermolecules as hub proteins. The supermolecules can be identified as the proteins with a single binding state and multiple binding partners, which we call stable hub proteins.

This database provides construction of Large Urban Regions (LUR) in Russia. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rogov & Rozenblat, 2020 for more details) thus, changing a city position in a global urban hierarchy. In doing so we use four principal urban concepts (Pumain et al., 1992): political definition, morphological definition, functional definition and conurbation that we call Large Urban Region. We constructed Russian LURs using criteria such as population distribution, road networks, access to an airport, distance from a core, presence of multinational firms. In this database, we provide population data for LURs and their administrative units.

We study the behaviour and cognition of wild apes and other species (elephants, corvids, dogs). Our video archive is called the Great Ape Dictionary, you can find out more here www.greatapedictionary.com or about our lab group here www.wildminds.ac.uk We consider these videos to be a data ark that we would like to make as accessible as possible. While we are unable to make the original video files open access at the present time you can search this database to explore what is available, and then request access for collaborations of different kinds by contacting us directly or through our website. We label all videos in the Great Ape Dictionary video archive with basic meta-data on the location, date, duration, individuals present, and behaviour present. Version 1.0.0 contains current data from the Budongo East African chimpanzee population (n=13806 videos). These datasets are being updated regularly and new data will be incorporated here with versioning. As well as the database there is a second read.me file which contains the ethograms used for each variable coded, and a short summary of other datasets that are in preparation for subsequent version(s). If you are interested in these data please contact us. Please note that not all variables are labeled for all videos, the detailed Ethogram categories are only available for a subset of data. All videos are labelled with up to 5 Contexts (at least one, rarely 5). If you are interested in finding a good example video for a particular behaviour, search for 'Library' = Y, this indicates that this clip contains a very clear example of the behaviour.

453 terms358 definitions in French143 definitions in EnglishFrench-English pedology terminology, extracted from an INRA/CILF document and other sources (TERMIUM, Concise Oxford Dictionary of Earth Sciences, etc.). Records compiled using index file (from Mme BOUROCHE, INRA, corrections delivered 15/04/96) and then merged with trainee project work (POUIVE) to form database TERMSOL.XM8.Subject areas include: soil science, soil mechanics, geomorphology, geology, physical geography, meteorology, hydrology, hydrography, mineralogy

List of fields and definitions used in a data set of freshwater macroinvertebrates occurrence records from Cuba. Terms and definitions are according to Darwin Core standards (dc, http://www.tdwg.org/standards/450 ), Freshwater Core Template at Freshwater Biodiversity Data Portal (fwct, https://data.freshwaterbiodiversity.eu/) and Global Biodiversity Information Facility (gbif, https://www.gbif.org )

https://www.youtube.com/watch?v=A8syQeFtBKc

Context

The Stanford Mass Shootings in America (MSA) is a dataset released under Creative Commons Attribution 4.0 international license by the Stanford Geospatial Center. While not an exhaustive collection of mass shootings, it is a high-quality dataset ranging from 1966 to 2016 with well-defined methodology, definitions and source URLs for user validation.

This dataset can be used to validate other datasets, such as us-mass-shootings-last-50-years, which contains more recent data, or conduct other analysis, as more information is provided.

Content

This dataset contains data by the MSA project both from it's website and from it's Github account. The difference between the two sources is only on the data format (i.e. .csv versus .geojson for the data, or .csv versus .pdf for the dictionary).

• mass_shooting_events_stanford_msa_release_06142016
  • Contains a nonexaustive list of US Mass Shootings from 1966 to 2016 in both .csv and .geojson formats.
• dictionary_stanford_msa_release_06142016
  • Contains the data dictionary in .csv and .pdf formats. Note the .pdf format provides an easier way to visualize sub-fields.

Note the data was reproduced here without any modifications other than file renaming for clarity, the content is the same as in the source.

The following sections are reproduced from the dataset creators website. For more details, please see the source.

Project background

The Stanford Mass Shootings of America (MSA) data project began in 2012, in reaction to the mass shooting in Sandy Hook, CT. In our initial attempts to map this phenomena it was determined that no comprehensive collection of these incidents existed online. The Stanford Geospatial Center set out to create, as best we could, a single point repository for as many mass shooting events as could be collected via online media. The result was the Stanford MSA.

What the Stanford MSA is

The Stanford MSA is a data aggregation effort. It is a curated set of spatial and temporal data about mass shootings in America, taken from online media sources. It is an attempt to facilitate research on gun violence in the US by making raw data more accessible.

What the Stanford MSA is not

The Stanford MSA is not a comprehensive, longitudinal research project. The data collected in the MSA are not investigated past the assessment for inclusion in the database. The MSA is not an attempt to answer specific questions about gun violence or gun laws.

The Stanford Geospatial Center does not provide analysis or commentary on the contents of this database or any derivatives produced with it.

Data collection methodology

The information collected for the Stanford MSA is limited to online resources. An initial intensive investigation was completed looking back over existing online reports to fill in the historic record going back to 1966. Contemporary records come in as new events occur and are cross referenced against a number of online reporting sources. In general a minimum of three corroborating sources are required to add the full record into the MSA (as many as 6 or 7 sources may have been consulted in many cases). All sources for each event are listed in the database.

Due to the time involved in vetting the details of any new incident, there is often a 2 to 4 week lag between a mass shooting event and its inclusion in the public release database.

It is important to note the records in the Stanford MSA span a time from well before the advent of online media reporting, through its infancy, to the modern era of web based news and information resources. Researchers using this database need to be aware of the reporting bias these changes in technology present. A spike in incidents for recent years is likely due to increased online reporting and not necessarily indicative of the rate of mass shootings alone. Researchers should look at this database as a curated collection of quality checked data regarding mass shootings, and not an exhaustive research data set itself. Independent verification and analysis will be required to use this data in examining trends in mass shootings over time.

Definition of Mass Shooting

The definition of mass shooting used for the Stanford database is 3 or more shooting victims (not necessarily fatalities), not including the shooter. The shooting must not be identifiably gang, drug, or organized crime related.

Acknowledgements

The Stanford Mass Shootings in America (MSA) is a dataset released under [Creative Commons Attribution 4.0 int...

This Service Definition was created on request of C-5 Fire Department Assistant Fire Chief John Alquist on 1/24/2022, for anyone in working within the C-5 Bowie County Area for Hydrant InspectionIt works with the web map of C-5 Hydrants Map which supports the desktop web appliation C-5 Hydrants. Please use when working on the C-5 Hydrants desktop.The thumbnail uses a static QR code for easy scanning on a mobile device, to the map assocated with this service definition.All services except the image layer are hosted on ArcOnline and not affected by the authoritive data on GIS SDE, the image layer utilizes data from Texarkana Water Utilities GIS DepartmentThe information was created by:Pulling in the volunteer fire districts from the COG, and running a definition query to show only C-5.Pulling in Hydrants from the SDE (the authoritive data), and saving them in a Auxiliary database under a feature database called water.Pulling in Water Mains from the SDE (the authoritive data), and saving them in a Auxiliary database under a feature database called water.Selecting by location using the Auxiliary databases Hydrants and the Volunteer Fire Districts as the selecting item, give a 30 ft buffer.Then create a field in the attribute table called District.Run a mass calculation to chanage the selected to change the selected records in the District to say Near C-5.Run a another selecting by location using the Auxiliary databases Hydrants and the Volunteer Fire Districts as the selecting item, but do not give a buffer. Run a mass calculation to chanage the selected to change the selected records in the District to say C-5.Run a definition query to show only Districts equally to C-5 or Districts equally to Near C-5.Selecting by location using the Auxiliary databases Water Mains and the Volunteer Fire Districts as the selecting item, give a 30 ft buffer, select the check box to allow invert.The lines outside of the C-5 should be selected, delete them. Now go to the Water Mains from the Auxiliary database and change the symbology to be unique, and filter by pipe size. Change the sizes to range from 1 to 3.5 in line thickness with each size up a little darker blue. GIS uses three-step verification process for any correction, or addition.This web map is pulled from the listed layers below. Please note: This layer is NOT updated. If you encounter a issue with the addresses please reach out to us at the GIS department.See details on the right for when data was last updated categories and full length of credits. Layer availability is unaffected during updates.If you have any questions regarding ArcOnline, please feel free to reach out to the GIS department at gis@txkusa.orgLayersHydrants for C5Point layer WaterMainsForC5Polyline layerPolygon layerMunicipalBoundaries

The Clinical Practice Research Datalink (CPRD) is a large and widely used resource of electronic health records from the UK, linking primary care data to hospital data, death registration data, cancer registry data, deprivation data and mental health services data. Extraction and management of CPRD data is a computationally demanding process and requires a significant amount of work, in particular when using R. The rcprd package simplifies the process of extracting and processing CPRD data in order to build datasets ready for statistical analysis. Raw CPRD data is provided in thousands of.txt files, making querying this data cumbersome and inefficient. rcprd saves the relevant information into an SQLite database stored on the hard drive which can then be queried efficiently to extract required information about individuals. rcprd follows a four-stage process: 1) Definition of a cohort, 2) Read in medical/prescription data and save into an SQLite database, 3) Query this SQLite database for specific codes and tests to create variables for each individual in the cohort, 4) Combine extracted variables into a dataset ready for statistical analysis. Functions are available to extract common variable types (e.g., history of a condition, or time until an event occurs, relative to an index date), and more general functions for database queries, allowing users to define their own variables for extraction. The entire process can be done from within R, with no knowledge of SQL required. This manuscript showcases the functionality of rcprd by running through an example using simulated CPRD Aurum data. rcprd will reduce the duplication of time and effort among those using CPRD data for research, allowing more time to be focused on other aspects of research projects.

Trusted Research Environments (TREs) enable analysis of sensitive data under strict security assertions that protect the data with technical organizational and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks & their slight technical variations. To shine light on these problems, we give an overview of existing, publicly described TREs and a bibliography linking to the system description. We further analyze their technical characteristics, especially in their commonalities & variations and provide insight on their data type characteristics and availability. Our literature study shows that 47 TREs worldwide provide access to sensitive data of which two-thirds provide data themselves, predominantly via secure remote access. Statistical offices make available a majority of available sensitive data records included in this study.

Methodology

We performed a literature study covering 47 TREs worldwide using scholarly databases (Scopus, Web of Science, IEEE Xplore, Science Direct), a computer science library (dblp.org), Google and grey literature focusing on retrieving the following source material:

• Peer-reviewed articles where available,
• TRE websites,
• TRE metadata catalogs.

The goal for this literature study is to discover existing TREs, analyze their characteristics and data availability to give an overview on available infrastructure for sensitive data research as many European initiatives have been emerging in recent months.

Technical details

This dataset consists of five comma-separated values (.csv) files describing our inventory:

• countries.csv: Table of countries with columns id (number), name (text) and code (text, in ISO 3166-A3 encoding, optional)
• tres.csv: Table of TREs with columns id (number), name (text), countryid (number, refering to column id of table countries), structureddata (bool, optional), datalevel (one of [1=de-identified, 2=pseudonomized, 3=anonymized], optional), outputcontrol (bool, optional), inceptionyear (date, optional), records (number, optional), datatype (one of [1=claims, 2=linked records]), optional), statistics_office (bool), size (number, optional), source (text, optional), comment (text, optional)
• access.csv: Table of access modes of TREs with columns id (number), suf (bool, optional), physical_visit (bool, optional), external_physical_visit (bool, optional), remote_visit (bool, optional)
• inclusion.csv: Table of included TREs into the literature study with columns id (number), included (bool), exclusion reason (one of [peer review, environment, duplicate], optional), comment (text, optional)
• major_fields.csv: Table of data categorization into the major research fields with columns id (number), life_sciences (bool, optional), physical_sciences (bool, optional), arts_and_humanities (bool, optional), social_sciences (bool, optional).

Additionally, a MariaDB (10.5 or higher) schema definition .sql file is needed, properly modelling the schema for databases:

• schema.sql: Schema definition file to create the tables and views used in the analysis.

The analysis was done through Jupyter Notebook which can be found in our source code repository: https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb

This is a public repository to host a community generated database of published definitions for citizen science, as described in the peer-reviewed literature, government publications and policies, etc.

Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.

This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (MISO2) (Wiedenhofer et al. 2024). MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository.

The MAT_STOCKS database version 1.0 provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:

• Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)
• Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)
• Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)
• 177 countries
• 1900 to 2016

All units in kilotons. Paramter names are in accordance with the system definition given in the publication.

Additionally, this repository includes all data presented in the figures of the related journal article.

Further information

This dataset complements the following scientific article:

Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, https://doi.org/10.1111/jiec.13575

The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer dominik.wiedenhofer(a)boku.ac.at and visit our website to learn more about our project: MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.

France Debt: GG: State data was reported at 1,828.400 EUR bn in Jun 2018. This records an increase from the previous number of 1,808.800 EUR bn for Mar 2018. France Debt: GG: State data is updated quarterly, averaging 945.000 EUR bn from Dec 1995 (Median) to Jun 2018, with 91 observations. The data reached an all-time high of 1,828.400 EUR bn in Jun 2018 and a record low of 488.600 EUR bn in Dec 1995. France Debt: GG: State data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.F005: Central Government Debt: Maastricht Definition.

AnthWest is a large dataset, one of the outcomes of a comprehensive, broadly comparative study on the diversity, biology, biogeography, and evolution of Anthidium Fabricius in the Western Hemisphere. In this dataset a total of 22,648 adult occurrence records comprising 9,657 unique events are documented for 92 species of Anthidium, including the invasive range of two introduced species from Eurasia, A. oblongatum (Illiger) and A. manicatum (Linnaeus). The geospatial coverage of the dataset extends from northern Canada and Alaska to southern Argentina, and from below sea level in Death Valley, California, USA, to 4,700 m a.s.l. in Tucumán, Argentina. The majority of the records in the dataset correspond to information recorded from individual specimens examined by the authors during this project, and deposited into 60 biodiversity collections located in Africa, Europe, North and South America. A fraction (4.8%) of the occurrence records were taken from the literature, largely California records from a taxonomic treatment with some additional records for the two introduced species. The temporal scale of the dataset represents collection events recorded between 1886 and 2012. The data underpinning the analysis reported in this paper are deposited at GBIF, the Global Biodiversity Information Facility, http://ipt.pensoft.net/ipt/resource.do?r=anthidium. Resources in this dataset:Resource Title: AnthWest, Occurrence Records for Wool Carder Bees of the Genus Anthidium (Hymenoptera: Megachilidae, Anthidiini) in the Western Hemisphere (Data Dictionary). File Name: meta.xmlResource Description: The data dictionary for the Darwin Core Archive of AnthWest. Resource Title: Darwin Core Archive. File Name: dwca-anthidium-v7.0.zipResource Description: Zip file includes database field name definitions (meta.xml); also includes the data paper (eml.xml), and occurrence data (occurrence.txt, tab-separated ). Find web download at http://ipt.pensoft.net/ipt/archive.do?r=anthidium

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/ .

France Net Debt: GG: Local Government data was reported at 187.900 EUR bn in Mar 2018. This records a decrease from the previous number of 189.400 EUR bn for Dec 2017. France Net Debt: GG: Local Government data is updated quarterly, averaging 111.750 EUR bn from Dec 1995 (Median) to Mar 2018, with 90 observations. The data reached an all-time high of 189.400 EUR bn in Dec 2017 and a record low of 83.300 EUR bn in Jun 2002. France Net Debt: GG: Local Government data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.F005: Central Government Debt: Maastricht Definition.

France Net Debt: GG: State data was reported at 1,713.700 EUR bn in Jun 2018. This records an increase from the previous number of 1,691.200 EUR bn for Mar 2018. France Net Debt: GG: State data is updated quarterly, averaging 881.500 EUR bn from Dec 1995 (Median) to Jun 2018, with 91 observations. The data reached an all-time high of 1,713.700 EUR bn in Jun 2018 and a record low of 441.100 EUR bn in Dec 1995. France Net Debt: GG: State data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.F005: Central Government Debt: Maastricht Definition.