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

Chemistry Search Engine Market Outlook

The global chemistry search engine market size is set to witness robust growth, expanding from $1.2 billion in 2023 to an impressive $3.4 billion by 2032, driven by a compound annual growth rate (CAGR) of 12.1%. This substantial growth is primarily fueled by the increasing demand for efficient and accurate chemical data retrieval and the rising investment in scientific research and technological advancements in the field of chemistry.

One major growth factor for the chemistry search engine market is the burgeoning need for streamlined data management in academic and industrial research. With the exponential increase in scientific data, researchers require sophisticated tools to efficiently retrieve, analyze, and utilize chemical information. Chemistry search engines provide a centralized platform that simplifies the search process, enhances data accuracy, and saves valuable research time, thus driving their adoption in research institutions and laboratories worldwide.

Another significant driver is the pharmaceutical industryÂ’s increasing reliance on advanced search engines to expedite drug discovery and development processes. As pharmaceutical companies continue to innovate and develop new drugs, the need for quick and accurate access to chemical databases and scientific literature becomes paramount. Chemistry search engines facilitate this by offering comprehensive and specific search capabilities, enabling researchers to draw insights from vast datasets and make informed decisions more rapidly.

The chemical manufacturing sector also significantly contributes to the market's growth. With the industry's constant evolution, companies require robust search engines to stay updated with the latest chemical compounds, safety data sheets, and regulatory guidelines. By leveraging chemistry search engines, chemical manufacturers can enhance their research and development efforts, optimize production processes, and ensure regulatory compliance, thereby improving operational efficiency and innovation capabilities.

Regionally, North America is expected to dominate the market, owing to its strong research infrastructure, significant investment in R&D, and the presence of major pharmaceutical and chemical companies. The Asia Pacific region is anticipated to witness the highest growth rate, attributed to the expanding industrial base, increasing government funding for scientific research, and the rising number of academic institutions. Europe will also showcase substantial growth due to its well-established chemical and pharmaceutical sectors.

In the realm of chemical engineering, the integration of advanced software tools is becoming increasingly vital. Chemical Engineering Software plays a crucial role in enhancing the efficiency and accuracy of chemical processes. These software solutions are designed to simulate and analyze complex chemical reactions, optimize production processes, and ensure compliance with safety and environmental regulations. By leveraging these tools, chemical engineers can improve process design, reduce operational costs, and enhance product quality. The growing adoption of Chemical Engineering Software is driven by the need for innovation and efficiency in the chemical manufacturing sector, as companies strive to stay competitive in a rapidly evolving market.

Component Analysis

The chemistry search engine market is segmented by components into software and services. The software segment encompasses various types of chemistry search engines, including molecular search, spectral search, and structural search tools. These software solutions are designed to cater to the specific needs of researchers and scientists, offering features such as advanced search algorithms, data integration, and user-friendly interfaces. The increasing demand for efficient data retrieval and high accuracy in search results is driving the adoption of sophisticated software solutions in academia and industry.

The services segment includes implementation, training, maintenance, and support services provided by vendors to assist users in effectively deploying and utilizing chemistry search engines. These services are crucial for ensuring the optimal performance of search engines and for providing users with the necessary technical support and training. The growing complexity of chemical data and the need for seamless integration with existing systems have he

The dataset represents Emergency Medical Services (EMS) locations in the United States and its territories. EMS Stations are part of the Fire Stations / EMS Stations HSIP Freedom sub-layer, which in turn is part of the Emergency Services and Continuity of Government Sector, which is itself a part of the Critical Infrastructure Category. The EMS stations dataset consists of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Ambulance services are included even if they only provide transportation services, but not if they are located at, and operated by, a hospital. If an independent ambulance service or EMS provider happens to be collocated with a hospital, it will be included in this dataset. The dataset includes both private and governmental entities. A concerted effort was made to include all emergency medical service locations in the United States and its territories. This dataset is comprised completely of license free data. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. 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 12/29/2004 and the newest record dates from 01/11/2010.This dataset represents the EMS stations of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. An assessment of whether or not the total emergency medical services capability in a given area is adequate. 2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can determine those entities that are able to respond the quickest. 3. A resource for Emergency Management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for Federal Government events.

The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.

Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.

• The data we collect consists of two Primary files:

UltimateArabic: A file containing more than 193,000 original Arabic news texts, without pre-processing. The texts contain words, numbers, and symbols that can be removed using pre-processing to increase accuracy when using the dataset in various Arabic natural language processing tasks such as text classification.

UltimateArabicPrePros: It is a file that contains the data mentioned in the first file, but after pre-processing, where the number of data became about 188,000 text documents, where stop words, non-Arabic words, symbols and numbers have been removed so that this file is ready for use directly in the various Arabic natural language processing tasks. Like text classification.

• We have added two folders containing additional detailed datasets:

1- Sample: This folder contains samples of the results of web-scraping techniques for two popular Arab websites in two different news categories, Sports and Politics. this folder contain two datasets:

Sample_Youm7_Politic: An example of news in the "Politic" category collected from the Youm7 website. Sample_alarabiya_Sport: An example of news in the "Sport" category collected from the Al-Arabiya website.

2- Dataset Versions: This volume contains four different versions of the original data set, from which the appropriate version can be selected for use in text classification techniques. The first data set (Original) contains the raw data without pre-processing the data in any way, so the number of tokens in the first data set is very high. In the second data set (Original_without_Stop) the data was cleaned, such as removing symbols, numbers, and non-Arabic words, as well as stop words, so the number of symbols is greatly reduced. In the third dataset (Original_with_Stem) the data was cleaned, and text stemming technique was used to remove all additions and suffixes that might affect the accuracy of the results and to obtain the words roots. In the 4th edition of the dataset (Original_Without_Stop_Stem) all preprocessing techniques such as data cleaning, stop word removal and text stemming technique were applied, so we note that the number of tokens in the 4th edition is the lowest among all releases.

• The data is divided into 10 different categories: Culture, Diverse, Economy, Sport, Politic, Art, Society, Technology, Medical and Religion.

A set of online services created in support of MIRIAM, a set of guidelines for the annotation and curation of computational models. The core of MIRIAM Resources is a catalogue of data types (namespaces corresponding to controlled vocabularies or databases), their URIs and the corresponding physical URLs or resources. Access to this data is made available via exports (XML) and Web Services (SOAP). MIRIAM Resources are developed and maintained under the BioModels.net initiative, and are free for use by all. MIRIAM Resources are composed of four components: a database, some Web Services, a Java library and this web application. * Database: The core of the system is a MySQL database. It allows us to store the data types (which can be controlled vocabularies or databases), their URIs and the corresponding physical URLs, and other details such as documentation and resource identifier patterns. Each entry contains a diverse set of details about the data type: official name and synonyms, root URI, pattern of identifiers, documentation, etc. Moreover, each data type can be associated with several resources (or physical locations). * Web Services: Programmatic access to the data is available via Web Services (based on Apache Axis and SOAP messages). In addition, REST-based services are currently being developed. This API allows one to not only resolve model annotations, but also to generate appropriate URIs, based upon the provision of a resource name and accession number. A list of available web services, and a WSDL are provided. A browser-based online demonstration of the Web Services is also available to try. * Java Library: A Java library is provided to access the Web Services. The documentation explains where to download it, its dependencies, and how to use it. * Web Application: A Web application, using an Apache Tomcat server, offers access to the whole data set via a Web browser. It is possible to browse by data type names as well as browse by tags. A search engine is also provided.

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.

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.

This map service is a one-stop location to view and explore Kentucky geologic map data and related-data (geologic outcrops, photos, and diagrams), Kentucky water wells and springs, Kentucky oil and gas wells. All features are provided by the Kentucky Geological Survey via ArcGIS Server services. This map service displays the 1:500,000-scale geologic map of Kentucky at scales smaller than 1:100,000, and 1:24,000-scale geological quadrangle data at larger scales. The 1:500,000-scale geologic map data were derived from the 1988 Geologic Map of Kentucky, which was compiled by Martin C. Noger (KGS) from the 1981 Geologic Map of Kentucky (Scale 1:250,000) by McDowell and others (USGS). The 1:24,000-scale geologic map data and the fault data were compiled from 707 Geological Survey 7.5-minute geologic quadrangle maps, which were digitized during the Kentucky Geological Survey Digital Mapping Program (1996-2006).The basemap data is provided via ArcGIS Server services hosted by the Kentucky Office of Geographic Information.Some tools are provided to help explore the map data:- Query tool: use this tool to search on the KGS database of lithologic descriptions. Most descriptions are derived from the 707 1:24,000 geological quadrangle maps. Once a search is completed, every unit that contains the search parameters is highlighted on the map service.- ID tools: users can identify and get detailed info on geologic units and other map features using either the point, area, or buffer identification tools.A few notes on this service:- the legend is dynamic for the viewed extent. It is provided via a database call using the current map extent.- the oil and gas and water wells are ArcGIS Server services that update dynamically from the KGS database.- the geologic map and faults are dynamic ArcGIS Server map services.- the user can link to other geologic data for the viewed extent using the links provided in the "Geologic Info" tab.- you can query the entire KGS lithologic description database and highlight the relevant geologic units based on the query.

INTRODUCTION: This study focuses (Martins, 2021) on a database literature review (Sousa, Martins et al., 2016) of the neuronal proteome found both in neurotypical subjects and in the ICD-11 chapters 'Diseases of the Nervous System' and "Mental and Behavior Disorders", the Neuro.OralCard. This database offers an update of the OralCard (Arrais et al., 2013), revising 2416 neuronal proteins identified in neuroproteomics, from which 765 are present, detected, and identifiable in the saliva proteome. The saliva proteome database, published as the bioinformatics tool OralCard (Arrais et al., 2013), was obtained from SalivaTec, CIS, UCP (Rosa et al., 2012). This database, as a set of UniprotKB codes, was cross-mapped to the set of UniprotKB obtaining the Neuro.OralCard, the neuronal proteome database. METHODOLOGY: A systematic bibliographic search was performed using the search tool at PubMed's portal, complemented by Google Scholar searching utilities (Falagas et al., 2008). The following keywords were used as filters: "MeSH (Medical Subject Headings)", "neurological diseases", "psychiatric diseases", and "salivary diagnosis." In order to perform an ordered review based on the publications on molecular biology and the nervous system, additional keyword correlations and associations were searched. Only the publications from 2000 to 2021 were selected. Likewise, in order to perform an ordered review based on the correlation between the salivary proteins/nervous system proteins and physiological and pathologic conditions of neuropsychiatric conditions, the following search filters were also applied: "Humans" and "Clinical trial" in order to include, exclusively, articles of an experimental nature concerning the species Homo sapiens. Based on the extensive analysis (Neuro.OralCard References (2020). https://tinyurl.com/Neuro-OralCardReferences) of the 79 filtered and compiled articles, a survey of a representative sample of neuronal and salivary proteins identified in subjects with (i) neuropsychiatric conditions and (ii) healthy mental functioning was carried out. With the results obtained from this analysis, it was possible to create a neuronal proteome database named Neuro.OralCard. The full database has not yet been deposited in the oral proteome databank of the OralCard (Arrais, 2013). This specific analysis enables the updating of the OralCard, which is allusive to the human Oralia in different pathologic states. Meanwhile, the full neuroproteome (UniprotKB) was annotated in a non-restricted access server in the LIMMIT Lab, Faculty of Medicine, University of Lisbon, and accessible via: https://www.limmit.org/uploads/2/6/8/4/26841837/neuro.oralcard.xlsx (retrieved August 01, 2023). RESULTS: The final neuronal database, named Neuro.OralCard, revised not only neuronal produced but also peripheral proteins. These neuronal proteins ultimately aim to represent not only epigenome, transcriptome, proteome, and metabolome analysis but also the main findings of functional cellular assays. The results of this neurobiological approach (Martins et al., 2023) imply that alterations in neurotransmission, hormonal regulation, metabolism, the cell cycle, and the immune system may be partially responsible for neuronal and mental pathophysiology. The Neuro.OralCard database comprises 2416 proteins concerning ICD-10 neuropsychiatric conditions and mental health functioning. CONCLUSION: The Neuro.OralCard database aims to be used as a molecular data reference and support for biomarker discovery in neuropsychiatric illnesses and nonpathologic conditions of the nervous system.

BackgroundSurvivors of intimate partner violence (IPV) often face increased incidents of violence during stressful life events (SLEs) such as economic recessions, environmental disasters, and pandemics. These events can diminish the effectiveness of both formal (e.g., health, social, justice, labor, community) and informal (e.g., friends, family, neighbors) support systems. Additionally, SLEs exacerbate existing health and social inequities, making it necessary to understand the accessibility of support services during these times. This scoping review investigates access to services by individuals experiencing IPV during SLEs in high-income countries.ApproachA comprehensive search was conducted across several electronic databases including MEDLINE (OVID), Embase (OVID), PsychInfo (OVID), CINAHL (EBSCO), Global Health (EBSCO), Gender Watch (ProQuest), Web of Science, and Applied Social Sciences Index & Abstracts (ProQuest), along with the search engine Google Scholar. This search, which imposed no date restrictions, was extended through May 22nd, 2024. Key search terms were developed from prior literature and in consultation with an expert librarian, focusing on ‘stressful life events,’ ‘intimate partner violence,’ and ‘access to services.’. Each study was screened and extracted by two reviewers and conflicts were resolved through discussion or a third reviewer.ResultsThe search across eight databases and citation searching resulted in a total of 7396 potentially relevant articles. After removing 1968 duplicates and screening 5428 based on titles and abstracts, 200 articles underwent full abstract review. Ultimately, 74 articles satisfied the inclusion criteria and were selected for further analysis. The analysis focused on barriers and facilitators to access, identifying challenges within Survivors’ support systems, redirected resources during crises, and complex control dynamics and marginalization. Over 90% of the literature included covered the recent COVID-19 pandemic. Addressing these challenges requires innovative strategies, sustained funding, and targeted interventions for high-risk subgroups.ConclusionThis scoping review systematically outlined the challenges and enabling factors influencing the availability of support services for Survivors of IPV during SLEs. It underscores the need for robust, culturally sensitive health and social support mechanisms, and policies. Such measures are essential to better protect and assist IPV Survivors and their service providers during these critical times. Furthermore, it is imperative to integrate the insights and expertise of the violence against women (VAW) sector into emergency planning and policy-making to ensure comprehensive and effective responses that address the unique needs of Survivors in crises.

Hospitals in Kansas

The term "hospital" ... means an institution which-

(1) is primarily engaged in providing, by or under the supervision of physicians, to inpatients

(A) diagnostic services and therapeutic services for medical diagnosis, treatment, and care of injured, disabled, or sick persons, or (B) rehabilitation services for the rehabilitation of injured, disabled, or sick persons;

(...)

(5) provides 24-hour nursing service rendered or supervised by a registered professional nurse, and has a licensed practical nurse or registered professional nurse on duty at all times; ...

(...)

(7) in the case of an institution in any State in which State or applicable local law provides for the licensing of hospitals,

(A) is licensed pursuant to such law or (B) is approved, by the agency of such State or locality responsible for licensing hospitals, as meeting the standards established for such licensing;

(Excerpt from Title XVIII of the Social Security Act [42 U.S.C. § 1395x(e)], )

Included in this dataset are General Medical and Surgical Hospitals, Psychiatric and Substance Abuse Hospitals, and Specialty Hospitals (e.g., Children's Hospitals, Cancer Hospitals, Maternity Hospitals, Rehabilitation Hospitals, etc.).

TGS has made a concerted effort to include all general medical/surgical hospitals in Kansas. Other types of hospitals are included if they were represented in datasets sent by the state. Therefore, not all of the specialty hospitals in Kansas are represented in this dataset.

Hospitals operated by the Veterans Administration (VA) are included, even if the state they are located in does not license VA Hospitals.

Nursing homes and Urgent Care facilities are excluded because they are included in a separate dataset. Locations that are administrative offices only are excluded from the dataset.

Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries.

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] field. Based upon this field, the oldest record dates from 05/09/2006 and the newest record dates from 05/07/2008