With an emphasis on reaching historically underrepresented populations, the All of Us Research Program recruits adults aged 18 and above across the United States to share their health data to enable new insights into human health and research on precision medicine. Participants contribute electronic health records (EHR), survey responses, biospecimens, wearable devices (biometrics), and physical measurements.

The six All of Us surveys assess the areas listed below:

• Basic demographic information
• Lifestyle/substance use (i.e., tobacco, alcohol, and recreational drugs)
• Overall health (general health status, daily activities, and women’s health)
• Medical history (medical conditions and approximate age of diagnosis)
• Family medical history (medical history of immediate biological family members)
• Health care access and utilization (self-reported use of various health services)

There are currently three tiers of data access.

• Public Tier: Anonymized, aggregate data that can be viewed with the Data Browser.
• Registered Tier: Contains individual-level data and is available only to approved researchers on the Researcher Workbench. Authorized users also have access to tools such as the Cohort Builder, Jupyter Notebooks, and Dataset Builder.
• Controlled Tier: Includes genomic data in the form of whole genome sequencing and genotyping arrays, demographic data fields from EHRs and surveys that are suppressed in other tiers, and unshifted dates.

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

The Highway Statistics Series consists of annual reports containing analyzed statistical information on motor fuel, motor vehicle registrations, driver licenses, highway user taxation, highway mileage, travel, and highway finance. These information are presented in tables as well as selected charts. It has been published annually since 1945.

Food banks and food aid agencies help address food insecurity issues throughout the United States. This mission focused on understanding how critical infrastructure failures impact the function of food aid agencies and how the change in functioning changes food access. This research focused on five infrastructure systems -- transportation, electric power, communications, water, and the buildings or facilities utilized by food aid agencies to carry out their normal activities. The functioning of food aid agencies was broken down into three branches or domains that are critical for the operation of a food aid agencies. Specifically, food aid agencies need 1) people to help run the operation, 2) property or, more generally, a physical structure or structures, to house and conduct operations; 3) products or food stuffs to distribute. This mission includes five social science collections. The first two collections provide background on the planning and agenda for a focus group and the data collected from the focus group. The next three collections relate to an online survey of food aid agencies. These collections include the sample frame (a list of all active food aid agencies invited to participate in the survey), the primary (raw) data collected from the survey, and an example of a secondary (curated) dataset that focuses on critical infrastructure failures and changes in food aid agency functioning.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

    Feature
    Data Type
    Description
    Range or Example




    IP Address
    String
    IP address belonging to the login attempt
    0.0.0.0 - 255.255.255.255


    Country
    String
    Country derived from the IP address
    US


    Region
    String
    Region derived from the IP address
    New York


    City
    String
    City derived from the IP address
    Rochester


    ASN
    Integer
    Autonomous system number derived from the IP address
    0 - 600000


    User Agent String
    String
    User agent string submitted by the client
    Mozilla/5.0 (Windows NT 10.0; Win64; ...


    OS Name and Version
    String
    Operating system name and version derived from the user agent string
    Windows 10


    Browser Name and Version
    String
    Browser name and version derived from the user agent string
    Chrome 70.0.3538


    Device Type
    String
    Device type derived from the user agent string
    (mobile, desktop, tablet, bot, unknown)1


    User ID
    Integer
    Idenfication number related to the affected user account
    [Random pseudonym]


    Login Timestamp
    Integer
    Timestamp related to the login attempt
    [64 Bit timestamp]


    Round-Trip Time (RTT) [ms]
    Integer
    Server-side measured latency between client and server
    1 - 8600000


    Login Successful
    Boolean
    True: Login was successful, False: Login failed
    (true, false)


    Is Attack IP
    Boolean
    IP address was found in known attacker data set
    (true, false)


    Is Account Takeover
    Boolean
    Login attempt was identified as account takeover by incident response team of the online service
    (true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

ASNs with values >= 500.000

IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022) Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono. ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the GPLv3, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

ch_cantons.csv

• Mapping between Swiss Cantons and NUTS3 regions
• Source: https://en.wikipedia.org/wiki/Data_codes_for_Switzerland
• Extract from Terms of Use:

Creative Commons Attribution-ShareAlike 3.0 Unported License

• https://en.wikipedia.org/wiki/Data_codes_for_Switzerland

EIA_hydro_generation_2000_2014.csv

• Hydroelectricity generation per country and year
• Source: https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016
• Extract from Terms of Use:

Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."

• https://www.eia.gov/about/copyrights_reuse.php

GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid

This module incorporates an outdoor lesson, a hands-on lesson, and a web-based tool developed by the US Environmental Protection Agency (EPA) called the Eco-Health Relationship Browser. All of the files below are part of EPA Report # EPA/600/R-18/186.

Phylogenetic information inferred from the study of homologous genes helps us to understand the evolution of genes and gene families, including the identification of ancestral gene duplication events as well as regions under positive or purifying selection within lineages. Gene family and orthogroup characterization enables the identification of syntenic blocks, which can then be visualized with various tools. Unfortunately, currently available tools display only an overview of syntenic regions as a whole, limited to the gene level, and none provide further details about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. We present Aequatus, an open-source web-based tool that provides an in-depth view of gene structure across gene families, with various options to render and filter visualizations. It relies on precalculated alignment and gene feature information typically held in, but not limited to, the Ensembl Compara and Core databases. We also offer Aequatus.js, a reusable JavaScript module that fulfills the visualization aspects of Aequatus, available within the Galaxy web platform as a visualization plug-in, which can be used to visualize gene trees generated by the GeneSeqToFamily workflow.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html

A group blog providing expert, independent commentary on the personal genomics industry. The goal of the project is to provide genetic testing consumers with independent and informed analysis of developments in the field of genetics and the genetic testing industry. Members of Genomes Unzipped include active researchers in various fields of genetics, as well as specialists in the legal and public health issues surrounding new genomic technologies. Many of us have also been extensively involved in public communication about genetics. Members of the group have had their DNA tested with a variety of products. We have released all of these genetic data openly to the public, both as raw data and in a custom genome browser. As the project proceeds we plan to obtain more genetic tests ����?? up to and including whole genome sequencing ����?? and to continue to release these data to the world. The group is also performing analyses of our own raw genetic data to illustrate fundamental concepts in genetics, using software written both by group members and other collaborators; and we����??ll be releasing the code for that software in our new code repository. As the project expands, we����??ll be looking to add data from other volunteers to the project, as well as to collaborate with other ����??genome hackers����?? on the development of new tools for exploring genetic data.

Voucher Specimen and Tissue Collection This effort was motivated by the Oregon Biodiversity Genome Project (OBGP; www.obgp.org), a multi-institution collaboration between scientists and wildlife managers at Oregon State University, the Oregon Department of Fish and Wildlife (ODFW), and the United States Forest Service. The primary objective of the OBGP is to develop a regional genetic reference database to facilitate statewide eDNA monitoring programs for Oregon's resident freshwater fishes. The specific goals of the OBGP (Fig 2a) are to: (1) use sterile laboratory methods to collect 10 georeferenced full-bodied vouchers of each freshwater fish species from dispersed watersheds in Oregon; (2) archive and link voucher specimens, tissues, and metadata for taxonomic verification and revision; (3) sequence full mitogenomes from multiple specimens per species; and (4) make all curated data publicly available via a client-server database accessed via a web browser. The study area encompassed the State of Oregon—the region of interest for our eDNA monitoring program. We collected fishes in Oregon and expanded to a few sites in northern California and Washington State (Fig 2b). We examined historical location records in existing collections such as Oregon State Ichthyology Collection and conferred with local biologists to identify resident fishes and occupied locations. For cases where we knew or suspected that deeply divergent evolutionary lineages existed within the present concept of a species, we aimed to include representatives of all lineages. Biologists from ODFW ultimately identified 146 native and nonnative freshwater fish species and lineages that currently reside in Oregon and strategized collections to span watersheds throughout the state (Appendix S1). Each sampling kit (Appendix S2 Box S1) contained a 500-mL Nalgene bottle filled with 10% formalin, a 2.0 mL cryotube filled with 95% EtOH, a sterile scalpel, scissors and tweezers, a bleach wipe, latex gloves, a detailed sampling protocol to ensure consistent tissue sampling and data collection (Appendix S2 Box S2), and a field notes sheet (Appendix S2 Box S3) for metadata collection. Collectors anaesthetized and euthanized all fish specimens prior to tissue collection by immersion in an aqueous solution of Tricaine mesylate (MS-222). For collections in 2017, we worked with partners (Appendix S3 collecting_entity) who followed accepted procedures under Oregon State University and USFS IACUC protocols, but an IACUC was not required by all partner institutions. Specimen collection by ODFW was conducted under the agency's statutory management authority and in 2018, 2019, and 2020 ODFW collected specimens for ESA-listed species under National Oceanic and Atmospheric Administration Permit numbers 21780, 22639, and 23527 respectively. Fish under USFWS jurisdiction (i.e. fish that are neither marine nor anadromous) were covered under ODFW's ESA Section 6 Cooperative Agreement with USFWS. Details regarding partner collection permits and authority are listed in Appendix S3. We instructed all partners to collect a minimum of ~0.5 cm3 of tissue from each specimen, which was then placed in 95% EtOH for DNA extraction and sequencing. Euthanized fish were placed in 10% Formalin to ensure preservation of diagnostic features. When we failed to collect species or redundant examples of species, we augmented in-field collection with tissue samples loaned or gifted from North American ichthyology collections (OS14271, OS18056, OS18057, OS19982, OS19351, OS18993, OS20085, OS20084, OS20081, OS20080, OS20094, OS20088, OS20108, OS14271, OS22282, UW155929, UW158361, UAM:Fish:10376:401245, UAM:Fish:10464:374966, UAM:Fish:10464:374967). The goal of collecting 10 individuals per species was amended to collect three individuals and add specimens only if intraspecific genetic variation was detected in downstream mitogenome identity analyses (See below). Taxonomic Verification, Accession, and Cataloging ODFW biologists and partners identified specimens provisionally in the field and Oregon State Ichthyology Collection taxonomists verified and refined those identifications prior to cataloging the specimens by morphological examination and reference to published keys (Markle and Tomelleri 2016, Wydoski and Whitney 2003). The Oregon State Ichthyology Collection has arranged to accession all vouchers and tissues, with full-bodied voucher specimens being transferred from formalin to isopropyl alcohol for permanent storage. Tissues were stored in 2.0 mL cryotubes at -70°C in 95% EtOH. Accessioning and cataloging were ongoing at the time of writing. After generating sequence data (See below), we performed distance-based cluster analyses in Geneious to verify morphological identification 10.2.6 using default settings (Global alignment with free end gaps, Cost Matrix of 65% similarity, Tamura-Nei Genetic Distance Model, Neighbor-Joining (NJ) Tree build Method, Gap open penalty of 12, Gap e...

The Actuarial Information Browser is a web based tool that allows users to view actuarial data and other information regarding commodities insured under the Federal Crop Insurance program. The information is retrieved based on the following selectable criteria: reinsurance year, commodity, insurance plan, state and county. The information is displayed in reports, including but not limited to, rates, commodity prices, and special provisions.

DrosOMA - the Drosophila Orthologous Matrix browser presents the results of orthology delineation for 36 drosophilids from across the genus and four outgroup dipterans. All sequence data used to build the DrosOMA browser database were originally sourced from a public repository, the United States National Center for Biotechnology Information (NCBI). The sources for which have been compiled and are provided in this dataset.

Browser for viewing the Chippewa National Forest Stands data.Viewer documentation available here.File geodatabase updated monthlyIf symbology doesn't display as documented please try a different browser.Link to complete metadata report. This vector layer of stands data contains attribute information from the Forest Service FS-Veg module for mapping vegetation type (1:24,000) prepared by the Chippewa National Forest (CNF). This data is the current stand data production and includes only forest service lands.This data set displays the vegetation on Forest Service lands within the Chippewa National Forest, by stand. A stand is the basic management unit for Forest activities. It is recognized as an area where vegetation characteristics are relatively homogeneous and distinct from adjacent stands. Stands are characterized by their existing vegetation (EV) (forest type or vegetation type if non-forested) and other attributes.The forest is divided into districts. There are currently three legislated districts. Each district is divided into smaller administrative units called locations (or compartments). Each location is divided into stands, usually around 100. These data with their various attributes support forest management, project-level planning, decision making, and implementation and monitoring on the Chippewa National Forest.The FSVeg Stand data set is available free of charge for public and private use. The data is provided in an File Geodatabase format. A charge may apply for any processing or conversion. This data set represents the most current and complete data available at the time of acquisition. The user assumes responsibility for appropriate use of the data, recognizing that misuse may yield inaccurate or misleading results. The Forest Service reserves the right to edit, update, modify, or replace GIS data without notification.

Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .

Alameda County and surrounding area Hospitals with Bed Counts. Bed Count Source:* American Hospital Directory - https://www.ahd.com/states/hospital_CA.htmlDisclaimer: Bed count values are to be used only for exploratory analysis and demonstration purposes. Discrepancies may be found in actual bed count values.*Some bed counts taken from direct web browser searches where data was not available from exact match for hospital name.

This map uses American Community Survey (ACS) 5 year estimates for poverty and income through 2018. Educational attainment data comes from the ESRI demographic dataset. Please use this data browser for more info. The map shows how poverty, education, and income levels vary throughout Montana census tracts. It shows place-based socioeconomic disparities. This map is inspired by the following one located at the CDC Chronic Disease Exchange. For questions on the map please contact Brian.Norderud@mt.gov.

Digital Surface Model (DSM) of SP Crater, Arizona, created from stereo images captured by a Planet SkySat satellite. It is distributed as a single band Cloud-optimized GeoTiff, with each pixel representing the height at that location.

MaNIS is a database and infrastructure meant to facilitate open access to combined specimen data from a web browser, enhance the value of specimen collections, conserve curatorial resources, and use a design paradigm that can be easily adopted by other disciplines with similar needs. With support from the National Science Foundation, seventeen North American institutions and their collaborators developed the Mammal Networked Information System. The original objectives of MaNIS were to 1) facilitate open access to combined specimen data from a web browser, 2) enhance the value of specimen collections, 3) conserve curatorial resources, and 4) use a design paradigm that can be easily adopted by other disciplines with similar needs. As an NSF-funded initiative, MaNIS has achieved these objectives while avoiding the need for long-term, external maintenance of the network and centralized data management. As MaNIS faces the future, it is only through expansion of the network, both nationally and internationally, that the real impact of this collaborative effort will be maximized. Participation by other institutions is now welcome and those wishing to join have at their disposal the data standards, software and documentation that were developed for this project. All that is asked of future participants is that they make the same institutional commitment as the original collaborators to maintain their repositories of high-quality specimen collections and make the accompanying data available for the benefit of all. At the time of its inception, development of MaNIS addressed the urgent call for natural history museums to come together to build and support a biodiversity informatics infrastructure to facilitate and enhance research, education, conservation, and public health. That call has now been answered. It is hoped that continued expansion of the network will allow the preservation and sustainable use of biodiversity in all its complexity as we attempt to address the magnitude of human impacts on the Earth''s ecological systems during the 21st century. Supported by NSF