Database of beetle specimens preserved in the National Institute for Agro-Environmental Sciences (NIAES)

The U.S. Geological Survey (USGS), Woods Hole Science Center (WHSC) has been an active member of the Woods Hole research community for over 40 years. In that time there have been many sediment collection projects conducted by USGS scientists and technicians for the research and study of seabed environments and processes. These samples are collected at sea or near shore and then brought back to the WHSC for study. While at the Center, samples are stored in ambient temperature, cold or freezing conditions, depending on the best mode of preparation for the study being conducted or the duration of storage planned for the samples. Recently, storage methods and available storage space have become a major concern at the WHSC. The shapefile sed_archive.shp, gives a geographical view of the samples in the WHSC's collections, and where they were collected along with images and hyperlinks to useful resources.

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

Database for the analysis of the educational role of Citizen Science projects in the framework of Open Science from the paradigm of complex thinking

Motivation is of great importance in the teaching-learning process, because motivated students seek out opportunities and show interest and enthusiasm in carrying out their tasks. The objective of this review is to identify and present the information available in the literature on the status quo of motivation among nursing program entrants. This is a qualitative scoping review study, a type of literature review designed to map out and find evidence to address a specific research objective, following the Joanna Briggs Institute methodology. The objective was outlined using the PCC (Population, Concept, Context) acronym. The protocol was developed and registered on the Open Science Framework (OSF) platform under DOI 10.17605/OSF.IO/EJNGY. The search strategy and database selection were defined by a library and information science professional together with the authors. The search will be carried out in the following databases: Cumulative Index to Nursing and Allied Health Literature, Literatura Latino Americana e do Caribe em Ciências da Saúde, Lilacs Esp, National Library of Medicine (PubMed), ScienceDirect, Scopus, and the Web of Science platform. The researchers will meet to discuss discrepancies and make decisions using a consensus model, and a third researcher will be tasked with independently resolving any conflicts. Data extraction will involve two independent researchers reviewing each article. Documents such as original articles; theoretical studies; experience reports; clinical study articles; case studies; normative, integrative, and systematic reviews; meta-analyses; meta-syntheses; monographs; theses; and dissertations in English, Portuguese, and Spanish from 2017 to 2023 were included. The results will be presented in tabular and/or diagrammatic format, along with a narrative summary.

The dataset was gathering from Web of Science on the 13th of June 2022. Each record related three conditions simultaneously: i) “social media” or internet-based applications, ii) “general education” or keywords related to this level of study and not contain other grade-related keywords, iii) related to school activities. Data was limited regarding collections, research areas, types of documents, languages, and published years. Then, the document content was validated. Each record was reviewed based on its title and summary information, which determined the paper's eligibility. After the data collection process, the dataset containing 2,122 records related to SMGE for 2005-2021 was formed.

A database of citizen science projects identified from Wikipedia's List of Citizen Science Projects, SciStarter and contributions from the ACTION consortium members. Updated to include

Open the Data Resource: https://star.chesapeakebay.net/ The Chesapeake Bay Program's Strategic Science and Research Framework (SSRF) was developed to identify and assess the partnership's short- and long-term science needs. These science needs are captured and tracked in this continually updated database. The science needs that are captured in this database were:

Identified as necessary to make progress toward a Chesapeake Bay Watershed Agreement goal or outcome, Expressed through the Chesapeake Bay Program's Strategy Review System process, and/or Listed as a recommendation within a Scientific and Technical Advisory Committee workshop report.

The Chesapeake Bay Program uses this database to engage stakeholders, identify opportunities to better align or evolve resources, update activities and workgroups to address needs, and inform STAC of its research priorities. This database can also be used by science providers to identify projects or collaborations of interest on which to engage the program. Science providers can represent a wide range of entities including, but not limited to, academic institutions, federal and state agencies, local entities, non-profit organizations and citizen science programs.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

This web page leads to a database of images and information about the 150 major impact craters on Callisto and is updated semi-regularly based on continuing analysis of Voyager images.

The dataset contains all the data produced running the research software for the study:"Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta".

Disclaimer: these results are not considered to be representative, because we have fount that Mega Journals skewed significantly some of the data. The result datasets without Mega Journals are published here.

Description of datasets:

SSH_Publications_in_OC_Meta_and_Open_Access_status.csv: containing information about OpenCitations Meta coverage of ERIH PLUS Journals as well as their Open Access availability. In this dataset, every row holds data for a Journal of ERIH PLUS also covered by OpenCitations Meta database. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

SSH_Publications_by_Discipline.csv: containing information about number of publications per discipline (in addition, number of journals per discipline are also included). The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

SSH_Publications_and_Journals_by_Country: containing information about number of publications and journals per country. The dataset has three columns, the first, labeled "Country", contains single countries of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

result_disciplines.json: the dictionary containing all disciplines as key and a list of related ERIH PLUS venue identifiers as value.

result_countries.json: the dictionary containing all countries as key and a list of related ERIH PLUS venue identifiers as value.

duplicate_omids.csv: a dataset containing the duplicated Journal entries in OpenCitations Meta, structured with two columns: "OC_omid", the internal OC Meta identifier; "issn", the issn values associated to that identifier

eu_data.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

eu_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of european countries. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_eu.csv: contains the data specific for European countries' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

us_data.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "Original_Title", "Country_of_Publication","ERIH_PLUS_Disciplines", "disc_count", the number of disciplines per Journal.

us_disciplines_count.csv: containing information about number of publications per discipline and number of journals per discipline of the United States. The dataset has three columns, the first, labeled "Discipline", contains single disciplines of the ERIH classificaton, the second and the third, labeled "Journal_count" and "Publication_count", respectively, the number of Journals and the number of Publications counted for each discipline.

meta_coverage_us.csv: contains the data specific for the United States' SSH Journals covered in OCMeta. It is structured with the following columns: "EP_id", the internal ERIH PLUS identifier; "Publications_in_venue", the numbers of Publications counted in each venue; "OC_omid", the internal OpenCitations Meta identifier for the venue; "issn", numbers of publications in each venue; "Open Access", a value to represent if the journal is OA or not, either "True" or "Unknown".

Abstract of the research:

Purpose: this study aims to investigate the representation and distribution of Social Science and Humanities (SSH) journals within the OpenCitations Meta database, with a particular emphasis on their Open Access (OA) status, as well as their spread across different disciplines and countries. The underlying premise is that open infrastructures play a pivotal role in promoting transparency, reproducibility, and trust in scientific research. Study Design and Methodology: the study is grounded on the premise that open infrastructures are crucial for ensuring transparency, reproducibility, and fostering trust in scientific research. The research methodology involved the use of secondary data sources, namely the OpenCitations Meta database, the ERIH PLUS bibliographic index, and the DOAJ index. A custom research software was developed in Python to facilitate the processing and analysis of the data. Findings: the results reveal that 78.1% of SSH journals listed in the European Reference Index for the Humanities (ERIH-PLUS) are included in the OpenCitations Meta database. The discipline of Psychology has the highest number of publications. The United States and the United Kingdom are the leading contributors in terms of the number of publications. However, the study also uncovers that only 38% of the SSH journals in the OpenCitations Meta database are OA. Originality: this research adds to the existing body of knowledge by providing insights into the representation of SSH in open bibliographic databases and the role of open access in this domain. The study highlights the necessity for advocating OA practices within SSH and the significance of open data for bibliometric studies. It further encourages additional research into the impact of OA on various facets of citation patterns and the factors leading to disparity across disciplinary representation.

Related resources:

Ghasempouri S., Ghiotto M., & Giacomini S. (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - RESEARCH ARTICLE. https://doi.org/10.5281/zenodo.8263908

Ghasempouri, S., Ghiotto, M., Giacomini, S., (2023). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - DATA MANAGEMENT PLAN (Version 4). Zenodo. https://doi.org/10.5281/zenodo.8174644

Ghasempouri, S., Ghiotto, M., Giacomini, S. (2023e). Open Science for Social Sciences and Humanities: Open Access availability and distribution across disciplines and Countries in OpenCitations Meta - PROTOCOL. V.5. (https://dx.doi.org/10.17504/protocols.io.5jyl8jo1rg2w/v5)

Dataset

This dataset was created by Ken_shaaaaaaark

Released under Database: Open Database, Contents: Database Contents

Contents

Additional file 9. List of species name entries with modified scientific names, collected from COCONUT and NPASS databases.

No description was included in this Dataset collected from the OSF

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

Download CBDB Standalone Database. The standalone version of the China Biographical Database (CBDB) contains data on over 420,000 men and women in MS ACCESS Format. Documentation is included. Project Website (2019-04-24)

The Biodiversity Science Database is a compilation of fish community data from DFO Science Surveys. Data includes: sampling site, date, fish counts, fish species, and associated habitat information.

Question Paper Solutions of chapter Distributed Databases of Database Management Systems, 6th Semester , Computer Science and Engineering

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.