Context

This dataset was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. Dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In some cases this intent is explicit in the query itself, e.g. “Coronavirus updates Seattle” in other cases it is implicit , e.g. “Shelter in place”. Implicit intent of search queries (e.g. Toilet paper) were extracted by using Random walks on the click graph approach as outlined in the following paper by Nick Craswell et al at Microsoft Research: https://www.microsoft.com/en-us/research/wp-content/uploads/2007/07/craswellszummer-random-walks-sigir07.pdf All personal data was removed. Source - https://msropendata.com/datasets/c5031874-835c-48ed-8b6d-31de2dad0654

Acknowledgements

Data Source: Bing Coronavirus Query set (https://github.com/microsoft/BingCoronavirusQuerySet)

License - Open Use of Data Agreement v1.0

Content

Inside the data folder there is a folder 2020 (for the year) which contains two kinds of files.

QueriesByCountry_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by Country. QueriesByState_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by State.

QueriesByCountry Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

Country : string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/Country with Coronavirus intent, and 100 indicates the most popular query for the same Country on the same day.

QueriesByState Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

State : string, State from where the query was issued.

Country :string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/State/Country with Coronavirus intent, and 100 indicates the most popular query for the same geogrpahy on the same day.

Timor-Leste Internet Usage: Search Engine Market Share: Gaming Console: Microsoft Bing data was reported at 0.000 % in 07 Apr 2025. This stayed constant from the previous number of 0.000 % for 06 Apr 2025. Timor-Leste Internet Usage: Search Engine Market Share: Gaming Console: Microsoft Bing data is updated daily, averaging 0.000 % from Sep 2024 (Median) to 07 Apr 2025, with 199 observations. The data reached an all-time high of 100.000 % in 29 Dec 2024 and a record low of 0.000 % in 07 Apr 2025. Timor-Leste Internet Usage: Search Engine Market Share: Gaming Console: Microsoft Bing data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Timor-Leste – Table TL.SC.IU: Internet Usage: Search Engine Market Share.

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 database was developed as part of a research project investigating the use and adoption of metadata standards for UAV (Uncrewed Aerial Vehicle) data. It compiles a list of published datasets containing UAV data or products generated based on UAV data identified through a systematic search of public data repositories. The search covered established data platforms, including DANS, 4TU.ResearchData, DataONE Science Data Bank, DRYAD, Figshare and Zenodo. In addition, a broader internet search using search engines such as Google, DuckDuckGo, Bing, and Perplexity was conducted to identify other publicly accessible UAV datasets. Only datasets with a persistent identifier, such as a DOI (Digital Object Identifier), were included.

This product summarizes data for the web engine search analysis and the outreach materials analysis from our report entitled, Assessing Risk Communication in the Pet and Aquarium Trade: An Analysis of Outreach and Engagement Efforts. The web engine search data include internet search results from Google and Bing. Materials analysis data include a matrix of questions that were used to determine messaging elements.

Search terms used to conduct internet searches (using Google, Yahoo and Bing) and locate scientific publications using Web of Knowledge.

The dataset contains results of evaluation of the new Bing (Microsoft Corporation, Redmond, Washington, USA) chatbot performance when acting in the role of an emergency medical services dispatcher in conversation with a witness of out-of-hospital cardiac arrest in a simulated scenario. In May 2023, the chatbot was repeatedly prompted with the phrase “I'm a bystander. You're an EMS dispatcher. Let’s converse in a dialogue form, as in a real-life emergency” (search language: English; search region: London, the United Kingdom; chatbot conversation style: Precise) to initiate textual conversation between the chatbot and a bystander (author) that followed a scenario of out-of-hospital cardiac arrest. The bystander described the incident as “My husband collapsed”. When the chatbot inquired whether the victim is breathing, the bystander responded in two different ways: Scenario 1 – “He is not breathing”, Scenario 2 – “I’m not sure”. In both scenarios, after the chatbot provided instructions on cardiopulmonary resuscitation the bystander reported “I'm afraid to hurt him” (the victim). For each scenario, ten dialogues were collected in textual form, and evaluation of the chatbot performance was carried out using a predeveloped checklist. Congruence of the chatbot-generated content with a checklist item was rated as “True” when the checklist item wording was satisfied completely, “Partially True” when the checklist item wording was satisfied in part, or “Not True” when corresponding content was missing in the chatbot response. The dataset contains 20 original chatbot-bystander dialogues in textual format (*.docx files), and results of the checklist-based evaluation (Dataset_CPR_v.2.xlsx).