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 Financial Processing Centers layer consists of the following> Defense Finance and Accounting Services (DFAS) > DFAS like services associated with non DOD Federal Agencies > State Government Payment Centers > Internal Revenue Service payment centers (the IRS calls these 'Taxpayers Assistance Centers' > Check clearing houses including Federal Reserve locations that act as check clearing houses > Credit card payment processing centers > Defense Finance and Accounting Services provide accounting and finance services for military departments and defense agencies. Non-Defense Federal Government Equivalent agencies provide accounting and finance services for non-military government agencies. Internal Revenue Service Taxpayers Assistance Centers provided payment arrangements, account inquiries, adjustments, tax forms and preparation, and accepts payments. State Government Payment Centers provide payroll services for state government employees. Credit card clearinghouses participate in the transfer of funds for a credit card. And check clearinghouses participate in the transfer of funds for a check transaction. The basis of this dataset was information gathered from official internet websites for the agencies represented in this dataset, as well as other public domain and open source research. The name, address, phone number and geospatial location for 90% of the entities were completely verified by TGS. The locations for the balance of the entities were assigned using automated methods. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute the oldest record dates from 09/25/2006 and the newest record dates from 10/02/2006.

This dataset includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative offices buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this data was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g. schools, colleges, prisons, and libraries). Also excluded are state capitol buildings. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name, have had their [NAME] attribute set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] attribute was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [AREA] and [PHONE] fields have been left blank. All entities located on military bases have been removed from this dataset. The text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 2007/12/03 and the newest record dates from 2007/12/06.

State Government Buildings in the United States This dataset is comprised of buildings or properties that are owned or leased by state level governments. It includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative office buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this dataset was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g., schools, colleges, prisons, and libraries). Also excluded are state capitol buildings, as these entities are represented in other HSIP layers. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name have had their [NAME] value set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] value was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [TELEPHONE] field has been left blank. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 11/27/2007 and the newest record dates from 05/28/2008.

911 Public Safety Answering Point (PSAP) service area boundaries in New Mexico According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such system from within a military base it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPS are (i.e. load balancing), operational status (i.e. redundancy), or time of date / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases this was geospatial information, in others it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g. city and county boundaries). In some cases existing boundaries had to be modified to reflect coverage areas (e.g. "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county, and the homes and businesses along a road which is partly in another county. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

In Pugalur – a small village in Tamil Nadu, Swami a 55 year old farmer goes out to withdraw some money from his bank account. He takes out a smart card from his pocket and gives it to a business correspondent – a bank appointed agent who comes to the village with an electronic handheld device connected to the bank. The business correspondent takes Swami’s smart card and inserts it on his hand held device facilitating withdrawal of his money, deposits and other transactions.

Smart cards have not only changed Swami’s life but also the lives of millions of Indians living in remote villages who previously had no access to any kind of financial services. Be it an identity card, credit card, drivers license, health insurance card or a metro pass; smart cards are not only rapidly replacing paper and magnetic stripe cards wherever they are in use but have also started penetrating into sectors that had remained untapped so far.

In technology terms, smart cards resemble similar to “dumb” magnetic stripe cards, but with one major difference: embedded in them is a computer chip, either to process data held on the card, or to act as an access key to data that is held remotely. Smart cards are more secure than simple plastic or magnetic stripe cards and are more versatile, being able to store more data and operate multiple applications.

Till recently, the telecom sector has been the only prominent user of smart cards in the country. The picture is now undergoing a radical change. Driven by a number of public and private initiatives, the use of smart cards is getting more and more diversified. During 2013-2018, we expect smart cards to further percolate into a number of other sectors such as credit/debit cards, financial inclusion, public distribution, healthcare, identity management, transportation, etc. The versatile application of smart cards can be further validated from the fact that the telecom sector, which represented the biggest application sector in 2012, accounted for more than 70% of the total market volumes. In contrast, the National Population Register, which is expected to represent the biggest application segment in 2018, is expected to account for less than 31% of the total market volumes by 2018.

IMARC’s new report entitled “Smart Card Industry in India: SIM, Identity, Banking, Transport, Healthcare, Pay TV, Loyalty & PDS” gives a deep insight into the Indian smart cards market. The research study serves as an analytical as well as a statistical tool to understand not only the market trends, structure, drivers and restraints but also the outlook of the market till 2018. This report aims to serve as an excellent guide for investors, researchers, consultants, marketing strategists, and all those who are planning to foray into the smart card industry India in some form or the other.

What We Have Achieved in this Report

• Comprehensive situation analysis of the Indian smart cards market and its dynamics.
• Identifying all application segments/sub-segments and quantifying their current and future market potential.
• Providing a robust long range value and volume forecast for all segments and sub-segments.
• Providing an understanding of key drivers and restraints and their impact on current and future market scenario.

Smart Card Application Segments and Sub-segments Covered in this Report

• Telecommunication
• National Population Register Project
• Public Distribution System
• Pay TV
• Loyalty Cards
• Financial Services
  • Credit / Debit Cards
  • Financial Inclusion
  • PAN Cards
• Travel Identity
  • Driving License
  • Vehicle Registration Certificates
  • E-Passports
• Automated Fare Collection
  • Metro Rail Projects
  • Bus Projects
  • Indian Railways
• Healthcare
  • Rashtriya Swasthya Bima Yojna
  • Other Healthcare Applications

Focus of the Analysis for Each Segment and Sub-segment

• Segment/Sub-segment Overview
• Smart Card Implementation Scenario
• Historical and Future Smart Card Volume Demand
• Historical and Future Smart Card Value Demand

Research Methodology

• Initial Exploration of the Indian Smart Cards Market: Conducted primary and secondary market research to complement/enhance our current knowledge and to identify key market segments and sub-segments.
• Qualitative Market Research: Interviewed various industry stakeholders to gain a comprehensive insight into all major segments and sub segments. This included understanding key metrics and events such as smart card requirements, current and future demand, implementation timelines, success & risk factors, costs, etc.
• Quantifying the Current and Future Market Potential: Consolidated our results to quantify the value and volume potential of smart cards in each segment and sub-segment.
• Validating Our Results: Collaborated with industry stakeholders to validate our results and findings.

Information Sources

Information has been gleaned from both primary and secondary sources:

• Primary sources include industry surveys and face to face/telephone interviews with industry experts.
• Secondary sources include proprietary databases and search engines. These sources include company websites, reports, books, trade journals, magazines, white papers, industry portals, government sources and access to more than 4000 paid databases.

State Government Buildings in Oregon. This dataset is comprised of buildings or properties that are owned or leased by state level governments. It includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative office buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this dataset was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g., schools, colleges, prisons, and libraries). Also excluded are state capitol buildings, as these entities are represented in other HSIP layers. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name have had their [NAME] value set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] value was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [TELEPHONE] field has been left blank. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 11/27/2007 and the newest record dates from 05/28/2008.

Government Electronic Directory Services (GEDS) provides public access to Government of Canada employee contact information as provided by participating departments. Encoded with the Latin Alphabet 1 (ISO 8859-1) character set. This is the dataset that contains all of the raw data within the Government of Canada Employee Contact Information system, not the searchable lookup. To search for contact information, please go to Government Electronic Directory Services (GEDS). (​​http://geds.gc.ca)

911 Public Safety Answering Point (PSAP) service area boundaries in the United States According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such a system from within a military base, it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems, or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise, military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases, areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPs are (i.e., load balancing), operational status (i.e., redundancy), or time of day / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases, this was geospatial information; in other cases, it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g., city and county boundaries). In some cases, existing boundaries had to be modified to reflect coverage areas (e.g., "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county plus the homes and businesses along a road which is partly in another county. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1) A disaster has struck, or is predicted for, a locality. The PSAP that may be affected must be identified and verified to be operational. 2) In the event that the local PSAP is inoperable, adjacent PSAP locations could be identified and utilized.

We developed this database to identify sampling units (entities related to Chemical Science education and career in Kerala) for the study 'Women’s Career Pathway in Chemical Sciences; a Multi-stage Investigation in Kerala' using two approaches:

Using search engines and visiting official databases of government departments and institutional websites.

By asking faculties, researchers and students in the Chemical Science field to supplement the list generated by the first approach.

We hope that this database will be useful for researchers in the field and students who wish to pursue their careers in Chemical Sciences.

All authors contributed equally to this work.

This research is supported by the Royal Society of Chemistry Inclusion and Diversity Fund, 2020