The dataset consists of archaeological object finds recoverd by members of the public, mostly by hobby metal-detecting, and recorded by the Finnish Heritage Agency (FHA) 2000-2023. This is a static research data dump opened to accompany the article:

Oksanen, Eljas and Wessman, Anna. 2025. New Horizons in Understanding Finnish Iron Age Material Culture Through Metal-detected Finds, Internet Archaeology 68. https://doi.org/10.11141/ia.68.4

The dataset consists of three chronological parts, or finds recorded that were in:

2000-2015 - Finds reported by the public and redeemed by the FHA before the current recording scheme was initiated. The finds were originally recorded as PDF finds lists in the 'Muinaiskalupäiväkirja' data service by the FHA. These were digitized in the DeepFIN project in 2021 by Taika-Tuuli Kaivo and Eljas Oksanen. Only post-Stone Age public finds were digitized. The original data was recorded using non-controlled vocabularies, and was translated where possible to the current standard Finnish archaeological vocabulary MAO/TAO (Ontology for Museum Domain and Applied Arts) during the DeepFIN digitization process. For MAO/TAO, see: https://finto.fi/maotao/fi/.

2015-2020 - The FHA launched the data service 'Luettelointisovellus' for recording public finds in 2015. The first data dump of its database was obtained 17th November 2020. Many of these finds were recorded before the FHA's internal archaeological vocabulary (which can be directly mapped to MAO/TAO) was developed and taken into use.

2020-2023 - A second data dump was obtained from the 'Luettelointisovellus' database on 8th May 2023. These newer records are in accordance with the current archaeological ontologies.

As part of the reserch project, Oksanen and Wessman lightly edited and cleaned the data, e.g., to remove typos and fill in missing date data. New fields were added to assist computational analysis (see Public_arch_finds_Finland_2000_2023_fields.csv).

Note therefore that the dataset is composed from multiple sources or datadumps, and the vocabulary (particularly for the object types in the 'find_name' field for the 2015-2020 data dump) is not fully harmonized to MAO/TAO standards. This was not required for the computational needs of the above article project.

The dataset contains spatial objects from the Nature Database associated with the programme of activities of the same name, General Species Finds. This group of activity types in the Nature Database concerns registrations of species finds that are not included in the context of Section 3 inspections or as an actual part of the State’s NOVANA monitoring. Records of species are thus included in all groups of organisms (plants, mammals, insects, birds, fungi, etc.).

This Guide is designed to assist you with adding and viewing data on a map within the Department of Climate Change, Energy, the Environment and Water's Find Environmental Data (FED) geospatial data catalogue.This Guide assumes that you are familiar with locating data within FED. For further assistance see the Finding Data Guide.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Stop relying on outdated and inaccurate databases and let Wiza be your source of truth for all deal sourcing and founder / CEO outreach.

Why we're different: The search fund market is dynamic and competitive - Wiza is not a static financial database that gets refreshed on occasion. Every datapoint is sourced and verified the moment that you receive the information. We verify deliverability of every single email ahead of providing the data, and we ensure that each person in your dataset has 100% job title and company accuracy by leveraging Linkedin Data sourced through their live Linkedin profile.

Key Features:

Comprehensive Data Coverage: Stop contacting the same people as everyone else. Wiza's search fund Data is sourced live, not stored in a limited database. When you tell us the type of company or person you would like to contact, we leverage Linkedin Data (the largest, most accurate database in the world) to find everyone who matches your ICP, and then we source the contact data and company data in real-time.

High-Quality, Accurate Data: Wiza ensures accuracy of all datapoints by taking a few key steps that other data providers fail to take: (1) Every email is SMTP verified ahead of delivery, ensuring they will not bounce (2) Every person's Linkedin profile is checked live to ensure we have 100% job title, company, location, etc. accuracy, ahead of providing any data (3) Phone numbers are constantly being verified with AI to ensure accuracy

Linkedin Data: Wiza is able to provide Linkedin Data points, sourced live from each person's Linkedin profile, including Subtitle, Bio, Job Title, Job Description, Skills, Languages, Certifications, Work History, Education, Open to Work, Premium Status, and more!

Personal Data: Wiza has access to industry leading volumes of B2C Contact Data, meaning you can find gmail/yahoo/hotmail email addresses, and mobile phone number data to contact your potential partners.

This dataset contains data collected and organised at Tallinn University, as a part of the MetDect project ("Metal-detected past: a study of long-term developments in settlement patterns, technology and visual culture on the example of metal-detector finds from Estonia" funded by the European Commission, Grant Agreement ID: 101003387). The main dataset contains information on metal-dected artefacts found in Estonia and identified by local archaeologists in the form of expert opinions (EH for short). Each record corresponds to one artefact, regardless of its condition (e.g. fragmented, burnt) or determination (e.g. unidentifiable), as long as it is recorded in the EH. Information is provided on artefact types, dates, contexts and general location. The MetDect dataset contains nearly 42,000 records. Sensitive information (excat location information) is not publicly available. The supplementary dataset shows how the EH-s are systematised for the main dataset. The number of EH-s is almost 800 and all EH-s were completed between 2013-2021.

An Open Context "tables" dataset item.

The development of the internet database which will fulfil the role of a catalogue of marble finds is supposed to enable other scholars to verify projects’s thesis formulated on the basis of the source material presented therein. Also, the creation of this dataset will allow other scholars to investigate different problems connected with marble.

The database contains basic qualitative and quantitative data about marble finds discovered in the southern Levant (Phoenicia, Palaestina, and Arabia). It takes into account marble finds used from the 1st century BCE to the 10 century AD. The database includes published finds with a specific chronology. I do not have the means to continuously update the data. However, I will try to add new materials or make important corrections, basing on your emails (m.gwiazda2 (at) uw.edu.pl). This database was created thanks to the publications of dozens of researchers, I hope that they has been recorded fully and correctly. If you are unhappy with the way I used your materials, please contact me.

I suggest to quote individual entries to the database in publications as follows:

The Project is financed by the National Science Centre (UMO-2020/37/B/HS3/00306).

Description:

Record number

Inventory number assigned arbitrary and automatically by the system. It is determined only by the order of cataloging tombs.

Site name

The contemporary name of the place where the tomb was discovered is given first. In brackets, the ancient names of these settlements are given.

Province

This field contains information on the name of the province during the reign of Justinian in which the object has been discovered.

Country

This field contains information about the name of the contemporary country in which the object has been discovered.

Context

In this place, information on the find context in given. Here featured: bathhouse, civic basilica, ordinary church, grave/cemetery, house, latrine, monastery, monastery church, pilgrimage church, press, public building, survey, synagogue, temple, workshop, and no data.

Site type

This field contains a simplified classification of settlements including: town, village, and monastery.

Port town

The field specifies whether the settlement was a seaport in antiquity.

Pilgrimage site

This field determines whether the site was associated with Christian pilgrimages.

Context description

This field contains additional information about the context in which the object was found.

Object type

This field contains a simplified classification of marble finds. Here featured: altar, ambo, architectural element, baptismal font, capital, capital (small), chancel screen, chancel post, column, column (small), column base, column base (small), menorah, mortar, opus sectile, pavement slab, pestle, plaque, reliquary, sarcophagus, sculpture, stela, table top, tombstone, unidentified, wall revetment, weight, varia, and vessel.

Stone colour

This field contains information about the colour of the object.

Provenance

This field contains information about the origin of the raw material based on laboratory analysis.

Analysis

This field contains information on whether the marble was examined by archaeometric methods.

Reused

This field contains information on whether the object was secondarily used in antiquity.

Inscription

This field contains information about the presence of the inscription on the object.

Object description

This field contains additional information about objects form.

Dating

The field contains the dating of the time of use of the marble object.

Dimensions

This field contains information about the size of the object.

Volume in cubic meter

This field contains information about the minimum volume of raw material in the form of cylinder or cuboid used to produce the object.

Map

This item contains information about the name of the contemporary country in which the tomb has been discovered.

In this place there is information on the context in which the tomb was discovered. Is the necropolis, on which it was discovered, associated with the city or the village. Separately treated are crypts built within monasteries or in churches.

References

Bibliographic references in most cases are limited to the latest publications, where you can also find references to older items.

Description of the INSPIRE Download Service (predefined Atom): Locations Animals of the Saarland (ABDS data) In addition to numerous in-house attributes, the following user-relevant attributes are available: OBJBEZ: Object type OBJART: Object type OBJBEM: Comment TOORT: Tatbe location TFINFO: Additional information about plant discovery site TFGAT: Genus TFART: Animal species TFDAT: Date of entry TFBEM: Comment on bat data METHODE: Method METHODELG: Method Langtext AUTOR: Author DETM:Determination secured STATUS: Status/behavioral HGEMSKK: Frequency HGEMSKL: Frequency scale ABSHAEUF: Number OF BUSINESS: Gender BELEG: Document QUELLE:Source RAND: Viewing object in the GDZ; MultiFeature class is composed of extensive feature class GDz2010.A_ntoft and the business table with the property data (GDZ2010.ntoft) — The link(s) for downloading the records is/are generated dynamically from getFeature Requests to a WFS 1.1.0

The development of the internet database which will fulfil the role of a catalogue of marble finds is supposed to enable other scholars to verify projects’s thesis formulated on the basis of the source material presented therein. Also, the creation of this dataset will allow other scholars to investigate different problems connected with marble.

The database contains basic qualitative and quantitative data about marble finds discovered in the southern Levant (Phoenicia, Palaestina, and Arabia). It takes into account marble finds used from the 1^st century BCE to the 10 century AD. The database includes published finds with a specific chronology. I do not have the means to continuously update the data. However, I will try to add new materials or make important corrections, basing on your emails (m.gwiazda2 (at) uw.edu.pl). This database was created thanks to the publications of dozens of researchers, I hope that they has been recorded fully and correctly. If you are unhappy with the way I used your materials, please contact me.

I suggest to quote individual entries to the database in publications as follows:

The Project is financed by the National Science Centre (UMO-2020/37/B/HS3/00306).

Description:

Record number

Inventory number assigned arbitrary and automatically by the system. It is determined only by the order of cataloging tombs.

Site name

The contemporary name of the place where the tomb was discovered is given first. In brackets, the ancient names of these settlements are given.

Province

This field contains information on the name of the province during the reign of Justinian in which the object has been discovered.

Country

This field contains information about the name of the contemporary country in which the object has been discovered.

Context

In this place, information on the find context in given. Here featured: bathhouse, civic basilica, ordinary church, grave/cemetery, house, latrine, monastery, monastery church, pilgrimage church, press, public building, survey, synagogue, temple, workshop, and no data.

Site type

This field contains a simplified classification of settlements including: town, village, and monastery.

Port town

The field specifies whether the settlement was a seaport in antiquity.

Pilgrimage site

This field determines whether the site was associated with Christian pilgrimages.

Context description

This field contains additional information about the context in which the object was found.

Object type

This field contains a simplified classification of marble finds. Here featured: altar, ambo, architectural element, baptismal font, capital, capital (small), chancel screen, chancel post, column, column (small), column base, column base (small), menorah, mortar, opus sectile, pavement slab, pestle, plaque, reliquary, sarcophagus, sculpture, stela, table top, tombstone, unidentified, wall revetment, weight, varia, and vessel.

Stone colour

This field contains information about the colour of the object.

Provenance

This field contains information about the origin of the raw material based on laboratory analysis.

Analysis

This field contains information on whether the marble was examined by archaeometric methods.

Reused

This field contains information on whether the object was secondarily used in antiquity.

Inscription

This field contains information about the presence of the inscription on the object.

Object description

This field contains additional information about objects form.

Dating

The field contains the dating of the time of use of the marble object.

Dimensions

This field contains information about the size of the object.

Volume in cubic meter

This field contains information about the minimum volume of raw material in the form of cylinder or cuboid used to produce the object.

Map

This item contains information about the name of the contemporary country in which the tomb has been discovered.

In this place there is information on the context in which the tomb was discovered. Is the necropolis, on which it was discovered, associated with the city or the village. Separately treated are crypts built within monasteries or in churches.

References

Bibliographic references in most cases are limited to the latest publications, where you can also find references to older items.

This resource contains records from Slovenian Fungal Database - Boletus informaticus on 31th March 2008. The resource represents the collected data on the species and distribution of fungi in Slovenia from the archives of the Mycological Association of Slovenia and the personal archives of its members, as well as, in part, from existing collections and literature sources. A computer program for inputting data on fungi in Slovenia is called Boletus informaticus. The program is aimed at systematically recording species of fungi, their distribution, and data regarding their habitat, in addition the program allows various ways processing materials, various means of data retrieval, and cartographical presentations of finds. When the related monograph (Dušan Jurc, Andrej Piltaver, Nikica Ogris. 2005. Fungi of Slovenia: species and distribution. Studia forestalia Slovenica, 124, Ljubljana, Slovenian Forestry Institute: 497 p.) was published in 2005, there were 114,620 records that describe distribution of 2,452 species of fungi in Slovenia. On 31th March 2008 the BI database had 160,757 records. These records give distribution to 2763 fungi species. All mentioned records are published here.

GIS-material for the archaeological project: Jonsbergshagen

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

description: 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/ .; abstract: 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/ .

The Organic INTEGRITY Database is a certified organic operations database that contains up-to-date and accurate information about operations that may and may not sell as organic, deterring fraud, increases supply chain transparency for buyers and sellers, and promotes market visibility for organic operations. Only certified operations can sell, label, or represent products as organic, unless exempt or excluded from certification. The INTEGRITY database improves access to certified organic operation information by giving industry and public users an easier way to search for data with greater precision than the formerly posted Annual Lists of Certified Operations. You can find a certified organic farm or business, or search for an operation with specific characteristics such as:

The status of an operation: Certified, Surrendered, Revoked, or Suspended The scopes for which an operation is certified: Crops, Livestock, Wild Crops, or Handling

The organic commodities and services that operations offer. A new, more structured classification system (sample provided) will help you find more of what you’re looking for and details about the flexible taxonomy can be found in the INTEGRITY Categories and Items list. Resources in this dataset:Resource Title: Organic INTEGRITY Database. File Name: Web Page, url: https://organic.ams.usda.gov/integrity/Default.aspx Find a specific certified organic farm or business, or search for an operation with specific characteristics. Listings come from USDA-Accredited Certifying Agents. Historical Annual Lists of Certified Organic Operations and monthly snapshots of the full data set are available for download on the Data History page. Only certified operations can sell, label or represent products as organic, unless exempt or excluded from certification.

Catholics per Parish {title at top of page}Data Developers: Burhans, Molly A., Cheney, David M., Emege, Thomas, Gerlt, R.. . “Catholics per Parish {title at top of page}”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Catholic Hierarchy, Environmental Systems Research Institute, Inc., 2019.Web map developer: Molly Burhans, October 2019Web app developer: Molly Burhans, October 2019GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/The Catholic Leadership global maps information is derived from the Annuario Pontificio, which is curated and published by the Vatican Statistics Office annually, and digitized by David Cheney at Catholic-Hierarchy.org -- updated are supplemented with diocesan and news announcements. GoodLands maps this into global ecclesiastical boundaries. Admin 3 Ecclesiastical Territories:Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Admin 3 Ecclesiastical Territories For Web”. Scale not given. Version 1.2. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Derived from:Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.

The Hemorrhagic Fever Viruses (HFV) sequence database collects and stores sequence data and provides a user-friendly search interface and a large number of sequence analysis tools, following the model of the highly regarded and widely used Los Alamos HIV database. The database uses an algorithm that aligns each sequence to a species-wide reference sequence. The NCBI RefSeq database is used for this; if a reference sequence is not available, a Blast search finds the best candidate. Using this method, sequences in each genus can be retrieved pre-aligned. Hemorrhagic fever viruses (HFVs) are a diverse set of over 80 viral species, found in 10 different genera comprising five different families: arena-, bunya-, flavi-, filo- and togaviridae. All these viruses are highly variable and evolve rapidly, making them elusive targets for the immune system and for vaccine and drug design. About 55,000 HFV sequences exist in the public domain today. A central website that provides annotated sequences and analysis tools will be helpful to HFV researchers worldwide.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

description: Storm Data is provided by the National Weather Service (NWS) and contain statistics on personal injuries and damage estimates. Storm Data covers the United States of America. The data began as early as 1950 through to the present, updated monthly with up to a 120 day delay possible. NCDC Storm Event database allows users to find various types of storms recorded by county, or use other selection criteria as desired. The data contain a chronological listing, by state, of hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning, high winds, snow, temperature extremes and other weather phenomena.; abstract: Storm Data is provided by the National Weather Service (NWS) and contain statistics on personal injuries and damage estimates. Storm Data covers the United States of America. The data began as early as 1950 through to the present, updated monthly with up to a 120 day delay possible. NCDC Storm Event database allows users to find various types of storms recorded by county, or use other selection criteria as desired. The data contain a chronological listing, by state, of hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning, high winds, snow, temperature extremes and other weather phenomena.

The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.