Database of bibliographic citations of multidisciplinary areas that covers various journals of medical, scientific, and social sciences including humanities.Publisher independent global citation database.

ObjectiveThis study aims to explore the clinical efficacy of mesenchymal stem cell (MSC) transplantation in the treatment of patients with spinal cord injury (SCI) through a network meta-analysis and to discuss the optimal transplantation strategy for treatment.MethodsWe conducted a computer search of clinical randomized controlled studies on MSC treatment for SCI in databases including PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Journal Database (VIP), Wanfang Database, and Chinese Biomedical Literature Service System (SinoMed) up to March 2024. Two researchers independently completed literature screening and data extraction according to the inclusion and exclusion criteria and used RevMan 5.4 software to assess the quality of the included studies. Network meta-analysis was performed using Stata 16.0 software.ResultsA total of 18 studies were included in the analysis. The results showed that MSCs significantly improved motor, sensory, and activities of daily living activities after SCI. Network meta-analysis indicated that umbilical cord mesenchymal stem cells (UCMSCs) were the most effective cell source, and intrathecal injection (IT) was the optimal transplantation method.ConclusionThe study suggests that the current use of UCMSCs for IT transplantation may be the best transplantation strategy for improving functional impairment after SCI. Further high-quality studies are still needed to validate the results of this study and to ensure the reliability of the results.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier [CRD42023466102].

Citation metrics are widely used and misused. We have created a publicly available database of over 100,000 top-scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator. Separate data are shown for career-long and single year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 sub-fields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least 5 papers. Career-long data are updated to end-of-2020. The selection is based on the top 100,000 by c-score (with and without self-citations) or a percentile rank of 2% or above.

The dataset and code provides an update to previously released version 1 data under https://doi.org/10.17632/btchxktzyw.1; The version 2 dataset is based on the May 06, 2020 snapshot from Scopus and is updated to citation year 2019 available at https://doi.org/10.17632/btchxktzyw.2

This version (3) is based on the Aug 01, 2021 snapshot from Scopus and is updated to citation year 2020.

Research findings indicate that programmed cell death (PCD) plays a pivotal role in the pathophysiology of spinal cord injury (SCI), and a recently discovered form of cell death, disulfidptosis, has emerged as a novel phenomenon. However, the characterization of disulfidptosis-related genes in SCI remains insufficiently explored. We retrieved SCI-related data from the Gene Expression Omnibus (GEO) database and identified three key genes associated with disulfidptosis in human SCI (CAPZB, SLC3A2, and TLN1), whose mediated signaling pathways are closely intertwined with SCI. Subsequent functional enrichment analysis suggested that these genes may regulate multiple pathways and exert corresponding roles in SCI pathology. Moreover, we predicted potential targeted drugs for the key genes along with their transcription factors and constructed an intricate regulatory network. CIBERSORT analysis revealed that CAPZB, SLC3A2, and TLN1 might be implicated in modulating changes within the immune microenvironment of individuals with SCI. Our study provides compelling evidence confirming the significant involvement of disulfidptosis following SCI while offering valuable insights into its underlying pathological mechanisms.

Research findings indicate that programmed cell death (PCD) plays a pivotal role in the pathophysiology of spinal cord injury (SCI), and a recently discovered form of cell death, disulfidptosis, has emerged as a novel phenomenon. However, the characterization of disulfidptosis-related genes in SCI remains insufficiently explored. We retrieved SCI-related data from the Gene Expression Omnibus (GEO) database and identified three key genes associated with disulfidptosis in human SCI (CAPZB, SLC3A2, and TLN1), whose mediated signaling pathways are closely intertwined with SCI. Subsequent functional enrichment analysis suggested that these genes may regulate multiple pathways and exert corresponding roles in SCI pathology. Moreover, we predicted potential targeted drugs for the key genes along with their transcription factors and constructed an intricate regulatory network. CIBERSORT analysis revealed that CAPZB, SLC3A2, and TLN1 might be implicated in modulating changes within the immune microenvironment of individuals with SCI. Our study provides compelling evidence confirming the significant involvement of disulfidptosis following SCI while offering valuable insights into its underlying pathological mechanisms.

Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house studies 3. Excel file with in house in vitro AR antagonism data. This dataset is associated with the following publication: Gray, L., J. Furr, C. Lambright, N. Evans, P. Hartig, M. Cardon, V. Wilson, A. Hotchkiss, and J. Conley. Quantification of uncertainties in extrapolating from in vitro androgen receptor (AR) antagonism to in vivo Hershberger Assay endpoints and adverse reproductive development in male rats. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(2): 297-311, (2020).

This Level 1 (L1) dataset contains the Version 3.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.0; https://doi.org/10.5067/CYGNS-L1X30. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.1 data: The CYGNSS science antenna gain patterns have been adjusted to improve the accuracy of the ocean surface scattering cross section (a.k.a. the NBRCS) calibration. They are adjusted so that the annual average observed NBRCS matches the model-predicted average as derived from Wavewatch-3 estimates of the surface roughness with the appropriate spectral tail extension added to the roughness spectrum. The adjustment is made independently at each position in the science antenna pattern. A correction for coarse quantization effects by the on-board digital processor has also been added. This reduces the effects of radio frequency interference, which appeared as calibration biases in the v3.0 L1 NBRCS and retrieval biases in the v3.0 L2 wind speed that were persistent at certain locations.

La base de données scientifique sur la biodiversité est une compilation de données sur les communautés de poissons provenant des enquêtes scientifiques du MPO. Les données comprennent : le site d'échantillonnage, la date, le dénombrement des poissons, les espèces de poissons et les informations associées sur l'habitat. Cette base de données a été créée pour soutenir la recherche sur les espèces de poissons en péril dans le cadre du Programme sur les espèces en péril du MPO et est principalement utilisée pour mettre à jour l'état actuel des populations d'espèces de poissons en péril dans le sud de l'Ontario. L'ensemble de données a été limité à l'échantillonnage des caractéristiques des sites pour le placement dans Great Lakes DataStream ; l'original est disponible via le portail des données ouvertes du gouvernement du Canada, voir l'URL de la source de données.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

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

The data science platform market size is projected to grow from USD 138 billion in 2024 to USD 1,678 trillion by 2035, representing a high CAGR of 25.47%.

Data obtained from computational DFT calculations on Tetragonal ScI is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

HabibAhmed/Data-Science-Instruct-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset was scraped from Indeed during the summer of 2024, focusing on the search term 'data scientist.' The data encompasses job listings from every state in the USA, including remote positions, providing a comprehensive snapshot of the data science job market during this period.

Working with this dataset involves a variety of skills that can help students gain valuable experience in data analysis, visualization, and interpretation. Some skills that could be practiced using this data:

1. Data Cleaning and Preprocessing
2. Exploratory Data Analysis (EDA)
3. Data Visualization
4. Text Analysis and Natural Language Processing (NLP)
5. SQL and Database Management
6. Geospatial Analysis
7. Machine Learning

Stack Exchange is a network of question-and-answer websites on topics in diverse fields, each site covering a specific topic, where questions, answers, and users are subject to a reputation award process. The reputation system allows the sites to be self-moderating.

The dataset here is specific to one such network site of Stack Exchange named Data Science Stack Exchange. The dataset is distributed over multiple files. It contains information on various Posts on data science that can be used for language processing, it has data on which posts are being liked by users more, etc. A lot of analysis can be done on this dataset.

The National Science Foundation (NSF) Surveys of Public Attitudes monitored the general public's attitudes toward and interest in science and technology. In addition, the survey assessed levels of literacy and understanding of scientific and environmental concepts and constructs, how scientific knowledge and information were acquired, attentiveness to public policy issues, and computer access and usage. Since 1979, the survey was administered at regular intervals (occurring every two or three years), producing 11 cross-sectional surveys through 2001. Data for Part 1 (Survey of Public Attitudes Multiple Wave Data) were comprised of the survey questionnaire items asked most often throughout the 22-year survey series and account for approximately 70 percent of the original questions asked. Data for Part 2, General Social Survey Subsample Data, combine the 1983-1999 Survey of Public Attitudes data with a subsample from the 2002 General Social Survey (GSS) (GENERAL SOCIAL SURVEYS, 1972-2002: [CUMULATIVE FILE] [ICPSR 3728]) and focus solely on levels of education and computer access and usage. Variables for Part 1 include the respondents' interest in new scientific or medical discoveries and inventions, space exploration, military and defense policies, whether they voted in a recent election, if they had ever contacted an elected or public official about topics regarding science, energy, defense, civil rights, foreign policy, or general economics, and how they felt about government spending on scientific research. Respondents were asked how they received information concerning science or news (e.g., via newspapers, magazines, or television), what types of television programming they watched, and what kind of magazines they read. Respondents were asked a series of questions to assess their understanding of scientific concepts like DNA, probability, and experimental methods. Respondents were also asked if they agreed with statements concerning science and technology and how they affect everyday living. Respondents were further asked a series of true and false questions regarding science-based statements (e.g., the center of the Earth is hot, all radioactivity is manmade, electrons are smaller than atoms, the Earth moves around the sun, humans and dinosaurs co-existed, and human beings developed from earlier species of animals). Variables for Part 2 include highest level of math attained in high school, whether the respondent had a postsecondary degree, field of highest degree, number of science-based college courses taken, major in college, household ownership of a computer, access to the World Wide Web, number of hours spent on a computer at home or at work, and topics searched for via the Internet. Demographic variables for Parts 1 and 2 include gender, race, age, marital status, number of people in household, level of education, and occupation.

SCI-Diabetes provides a fully integrated shared electronic patient record to support treatment of NHSScotland patients with Diabetes

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.

Profiles in Science presents the lives and work of innovators in science, medicine, and public health through in-depth research, curation, and digitization of archival collection materials.

This dataset includes full text and metadata describing nearly 30,000 items from Profiles in Science, including digitized correspondence, photographs, laboratory notebooks, diaries, and more that provide insight into the challenges and successes of scientific discovery and the variety of roles, paths, and perspectives involved.

The accompanying README.txt file, downloadable from the Attachments section below, provides a recommended citation and methodological information. The Data Dictionary provides documentation on the variable names, data types, and definitions.

Profiles in Science in its curated form is available at https://profiles.nlm.nih.gov/ as well as in NLM’s Digital Collections for exploration alongside other publicly available digital content, including books, film, prints, photographs, and manuscripts.

This dataset contains job postings related to Data Science roles in 2025, collected from publicly available sources. It includes essential details such as job titles, seniority levels, company information, locations, salaries, industries, company size, and required skills. The dataset has been cleaned and structured to ensure accuracy and consistency, with duplicates and irrelevant entries removed.

It is designed to help researchers, students, and professionals analyze hiring trends, salary ranges, and in-demand skills in the Data Science job market. This dataset can also support projects in machine learning, career prediction, salary forecasting, and workforce analytics.