This is a new, open, and transparent database of toxicokinetic data supporting EPA decision making. The database has already become the basis of research efforts within EPA to improve HTTK modeling using generic TK models and has facilitated the creation and validation of models for new exposure routes. Publishing the database supports open, transparent science and this database (the largest public database for this domain) will spur improvement and development of TK models by external experts in the field. Future efforts to improving the accessibility of this database (with a graphical user interface) and encouraging crowdsourcing to expand the size and scope of the database will lead to larger validation sets for our modeling efforts and likely lower uncertainties when estimating TK. This dataset is associated with the following publication: Sayre, R., J. Wambaugh, and C. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data. Springer Nature Group, New York, NY, 7: 122, (2020).

An architectural image dataset with 47K images. The database has two scene classes: outdoor and indoor. Users may request access to the data after accepting the full terms and conditions of using the database.

The Species of Greatest Conservation Need National Database is an aggregation of lists from State Wildlife Action Plans. Species of Greatest Conservation Need (SGCN) are wildlife species that need conservation attention as listed in action plans. In this database, we have validated scientific names from original documents against taxonomic authorities to increase consistency among names enabling aggregation and summary. This database does not replace the information contained in the original State Wildlife Action Plans. The database includes SGCN lists from 56 states, territories, and districts, encompassing action plans spanning from 2005 to 2022. State Wildlife Action Plans undergo updates at least once every 10 years by respective wildlife agencies. The SGCN list data from these action plans have been compiled in partnership with individual wildlife management agencies, the United States Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies. The SGCN National Database consists of three data tables: "source_data", "process_data", and "validated_data". Most users will likely find the "sgcn_species_all_records" table that combines all three tables most useful to compare "source_" names and "validated_" names and to aggregate and summarize using validated names. The "source_data" table provides an archive of all SGCN records listed by conservation authorities over multiple actions plans, which includes the scientific names, common names, locations, and year of action plan. The "process_data" table incorporates processing information, including the archiving and processing dates along with persistent identifiers used for record documentation, while the "validated_data" table provides the taxonomic identities from the matched taxonomic source, including the standardized scientific name, common name, and taxonomic ranks as well as links to supplementary taxonomic information.

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

The collective impact of humans on biodiversity rivals mass extinction events defining Earth's history, but does our large population also present opportunities to document and contend with this crisis? We provide the first quantitative review of biodiversity-related citizen science to determine whether data collected by these projects can be, and are currently being, effectively used in biodiversity research. We find strong evidence of the potential of citizen science: within projects we sampled (n = 388), ~1.3 million volunteers participate, contributing up to US Dollar 2.5 billion in-kind annually. These projects exceed most federally-funded studies in spatial and temporal extent, and collectively they sample a breadth of taxonomic diversity. However, only 12% of the 388 projects surveyed obviously provide data to peer-reviewed scientific articles, despite the fact that a third of these projects have verifiable, standardized data that are accessible online. Factors influencing publication included project spatial scale and longevity and having publically available data, as well as one measure of scientific rigor (taxonomic identification training). Because of the low rate at which citizen science data reach publication, the large and growing citizen science movement is likely only realizing a small portion of its potential impact on the scientific research community. Strengthening connections between professional and non-professional participants in the scientific process will enable this large data resource to be better harnessed to understand and address global change impacts on biodiversity.

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

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The On-premises segment was valued at USD 38.70 million in 2019 and showed

The Fuels Guide and Database for Big Sagebrush Ecological Sites was developed as part of the Joint Fire Sciences Program project “Quantifying and predicting fuels and the effects of reduction treatments along successional and invasion gradients in sagebrush habitats” (Shinneman et al. 2015). The research was carried out by the U.S. Geological Survey (USGS) Forest and Rangeland Ecosystem Science Center and Boise State University researchers, in partnership with the U.S. Bureau of Land Management and the Idaho Army National Guard. Most of the research for the project focused on the Morley Nelson Snake River Birds of Prey National Conservation Area (hereafter the NCA) in southern Idaho. Sagebrush shrublands in the NCA, and throughout much of the Great Basin and Snake River Plain, are highly influenced by non-native plants that alter successional trajectories, suppress native species, and promote frequent wildfire. Fine-fuel loadings created by nonnative annual grasses and forbs can be highly variable through space and time, which can increase uncertainty when predicting fire risk and behavior. The overarching goal of the research project was to explore and develop different approaches to better quantify and predict these dynamic fuel loadings, as well as the effects of fuels manipulations in sagebrush habitats. The purpose of this database is to provide a tool that allows ready access to fuel loading data across a range of conditions, from relatively intact sagebrush-bunchgrass communities to degraded communities dominated by nonnative annual grasses and forbs. The Fuels Guide and Database (FGD) is a tool designed to assist land managers in estimating fuel loads within a specific stand of vegetation, under conditions ranging from sagebrush-dominated to nonnative, annual grass/forb-dominated communities. Users can query the database based on vegetation cover, vegetation height, and specific environmental variables (for example elevation, precipitation, temperature, soil surface texture, and ecological site) and return fuel loading data that match the query parameters. The FGD also allows users to view photos by point or plot and to individually exclude certain points or plots to help identify areas that best match the current conditions. Final results can be exported to Microsoft Excel spreadsheet or summarized in Microsoft Word reports that can be used to improve estimates of fuel loadings in the field. Fuels data were collected on the NCA, and therefore extrapolation of queried results should also only be applied to the NCA and similar regional environments. However, there is potential for additional cover data, vegetation height data, and fuels data to be added to the FGD. If you are interested in contributing data to the FGD please contact the USGS Forest and Rangeland Ecosystem Science Center (fresc_outreach@usgs.gov). With additional input from other users, the Fuels Guide and Database has the potential to be a powerful tool throughout the sagebrush shrublands to assist land managers in quickly estimating fuel loadings.

The present database supports the study "A global database of seahorse research and innovation, from the beginning to 2022".

Scientific knowledge on seahorses is rapidly expanding in response to declines in wild populations due to habitat loss and fishing. A plethora of information has accumulated and up until now, a global, publicly available, curated database has never been produced in a transparent and systematic way. Here, we present the largest open-access repository of scientific publications addressing seahorses and, for the first time, of theses and patents. Compilation followed the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses, with modifications. The current repository duplicates the number of scientific publication records found from previous bibliometric/literature/review studies, using three extra repositories of source publications, and a lifetime window, e.i. from the beginning to March 2022. A total of 977 scientific publications, 101 theses and 533 patents are gathered in the dataset, covering 41 seahorse species out of 48 currently recognized. In addition, current work presents for the first time new metrics on authors, institutions, and research subject/field/discipline/thematic, as well as the organism’s stage of development (embryo, newborn, juvenile, subadult and adult). To expand metadata usage, the database was also made available in the Dublin Core™ Metadata Initiative format. This contribution can be used as a core reference for scientists, aquaculturists and conservationists, and is useful to rapidly identify relevant literature and knowledge gaps, better understand seahorse research and discover new trends in seahorse research and innovation.

The database is available in two formats:

1) SeahorseBibliometricDatabase.xlsx

and

2) SeahorseBibliometricDatabase_DublinCore.xlsx, which is a vocabulary standardized (Dublin Core™ Metadata Initiative) version of the previous one, for metadata reuse.

Dataset

This dataset was created by flo2607

Released under CC0: Public Domain

Contents

This dataset contains the International Surface Pressure Databank version 4.7 (ISPDv4), the world's largest collection of pressure observations. It has been gathered through international cooperation with data recovery facilitated by the ACRE Initiative and the other contributing organizations and assembled under the auspices of the GCOS Working Group on Surface Pressure and the WCRP/GCOS Working Group on Observational Data Sets for Reanalysis by NOAA Earth System Research Laboratory (ESRL), NOAA's National Climatic Data Center (NCDC), and the University of Colorado's Cooperative Institute for Research in Environmental Sciences (CIRES). The ISPDv4 consists of three components: station, marine, and tropical cyclone best track pressure observations. The station component is a blend of many national and international collections. In addition to the pressure observations and metadata, ISPDv4 contains feedback from the 20th Century Reanalysis version 3, including quality control information and uncertainty information.

Support for the International Surface Pressure Databank is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.

The International Surface Pressure Databank version 4.7 and 20th Century Reanalysis version 3 used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

which is the largest to-date and is also made available in public domain to advance much needed research in this area.

The Large River Monitoring Forum compiled fish assemblage data for five large rivers in the U.S. as a part of a coordinated effort to compare and contract river monitoring efforts in large river systems. Fish community data from five monitoring programs were integrated to create the standardized dataset. Authors: Timothy D. Counihan1, Ian R. Waite2, Andy Casper3, David Ward4, Jennifer Sauer5, Elise Irwin6, Colin Chapman7, Brian Ickes5, Craig Paukert8, John Kosovich9, and Jennifer M. Bayer10 1- United States Geological Survey, Western Fisheries Research Center, Columbia River Research Laboratory, 5501A Cook-Underwood Road, Cook, WA 98605; email:tcounihan@usgs.gov; Phone:509-538-2299; Fax:509-538-2843 2- United States Geological Survey, Oregon Water Science Center, 2130 S.W. Fifth Avenue Portland, OR 97201 3- Illinois Natural History Survey, Illinois River Biological Station, 704 N. Schrader, Havana IL 62644 4- United States Geological Survey, Upper Midwest Environmental Sciences Center 2630 Fanta Reed Road, La Crosse, Wisconsin 54603 5- United States Geological Survey, Grand Canyon Monitoring and Research Center, 2255 North Gemini Drive, Flagstaff, AZ 86001-1637 6- United States Geological Survey, Alabama Cooperative Fish and Wildlife Research Unit, School of Forestry and Wildlife Sciences, 602 Duncan Drive, Auburn University, Auburn, AL 36849-5418 7- Oregon Department of Fish and Wildlife, Northwest Regional Office, 17330 SE Evelyn Street, Clackamas, OR 97015 8- United States Geological Survey, Missouri Cooperative Fish and Wildlife Research Unit, Department of Fisheries and Wildlife, 302 Anheuser-Busch Natural Resources Building, University of Missouri, Columbia, MO 65211 9- U.S. Geological Survey, Core Science Analytics, Synthesis, & Libraries, Lakewood, CO 10- United States Geological Survey, Northwest Region & Pacific Northwest Aquatic Monitoring Partnership, 909 1st Ave, 8th Floor Seattle, WA 98104

Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.

An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field.

More details on the creation of the database are given in the following publication:

Bergman, S., Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large‐Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. Journal of Geophysical Research: Space Physics, 130. https://doi.org/10.1029/2024JA033568

Contact: S. Bergman, sofiabergmanphd@gmail.com

DNA Database Management Market Outlook

According to our latest research, the DNA Database Management Market size reached USD 2.87 billion globally in 2024, demonstrating robust expansion fueled by technological advancements and increasing adoption across multiple sectors. The market is expected to grow at a CAGR of 13.4% from 2025 to 2033, reaching a projected value of USD 8.91 billion by 2033. This impressive growth trajectory is primarily attributed to the rising demand for efficient genetic data storage, analysis, and retrieval systems, as well as the integration of artificial intelligence and cloud-based solutions in DNA database management platforms.

The primary growth driver of the DNA Database Management Market is the escalating need for advanced data management in forensic science and law enforcement. With the increasing volume of DNA data generated from crime scenes, missing person cases, and criminal investigations, law enforcement agencies globally are investing heavily in robust DNA database management systems to enhance crime-solving capabilities and streamline investigative processes. The proliferation of national and international DNA databases, such as CODIS in the United States and the National DNA Database in the UK, has further fueled the market’s expansion. Moreover, the growing public awareness regarding the benefits of DNA profiling in criminal justice and the adoption of legislative frameworks mandating DNA data storage are amplifying market growth.

Another significant factor propelling the DNA Database Management Market is the surge in healthcare and medical research applications. Hospitals, clinics, and research institutes are increasingly leveraging DNA databases for precision medicine, genetic disorder screening, and population genomics studies. The integration of next-generation sequencing technologies with sophisticated data management platforms has enabled the storage and analysis of massive genomic datasets, facilitating advancements in personalized treatment and early disease detection. Additionally, the COVID-19 pandemic underscored the importance of genomic surveillance, prompting substantial investments in healthcare infrastructure and database management solutions to monitor viral mutations and support vaccine development.

The growing interest in ancestry and genealogy among consumers is also contributing to the expansion of the DNA Database Management Market. Direct-to-consumer genetic testing companies, such as AncestryDNA and 23andMe, have popularized the concept of personal genomics, leading to an exponential increase in the volume of genetic data generated globally. These companies rely on advanced DNA database management systems to securely store, process, and analyze customer data, ensuring data privacy and regulatory compliance. The rising trend of consumer-driven genomics, coupled with the integration of big data analytics and machine learning algorithms, is expected to create new growth opportunities for market players in the coming years.

From a regional perspective, North America dominated the DNA Database Management Market in 2024, accounting for the largest share due to the presence of well-established healthcare infrastructure, strong government support for forensic science, and the widespread adoption of advanced IT solutions. Europe followed closely, driven by stringent regulatory frameworks and significant investments in biomedical research. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, propelled by increasing government initiatives, expanding healthcare sector, and rising awareness of genetic testing applications. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by improving healthcare access and growing emphasis on law enforcement modernization.

Component Analysis

The DNA Database Management Market by component is segmented into software, hardware, and services, each playing a crucial role in enabling comprehensive genetic data management solutions. Software solutions form the backbone of DNA database management, offering functionalities such as data storage, retrieval, analysis, and visualization. These platforms are designed to handle large-scale genomic datasets, ensuring efficient data integration, security, and interoperability with other healthcare and law enforcement systems. The evolution of cloud-based software and the integrat

Trends in International Mathematics and Science Study Advanced 1995 - TIMSS advanced 1995, the largest and most ambitious international study of student achievement conducted up to that time, was the first cycle of assessments of trends in students’ mathematics and science achievement, now known as the Trends in International Mathematics and Science Study. Questionnaires gathered extensive information about the teaching and learning of mathematics and science from students, as well as from their teachers and school principals. The study also investigated the mathematics and science curricula of the participating countries by conducting an analysis of curriculum guides, textbooks, and other curricular materials. TIMSS 1995 was conducted at five grade levels: students enrolled in the two grades containing the largest proportion of 9-year-old students (third and fourth grade in most countries) students enrolled in the two grades containing the largest proportion of 13-year-old students (seventh and eighth grade in most countries) students in their final year of secondary education Advanced: As an additional option, countries could test two special sub-groups of these students: students taking advanced courses in mathematics and/or students taking advanced courses in physics. For the advanced mathematics assessment, the target population consisted of students in their final year of secondary school who were taking, or had taken, courses in advanced mathematics. For physics, the target population was final-year secondary students who were taking, or had taken, courses in physics.

Content

Heavily cratered terrain on Mars was created between 4.2 to 3.8 billion years ago during the period of "heavy bombardment" (i.e. impacts of asteroids, proto-planets, and comets). Since then the surface of Mars has not been extensively modified. Surfaces on airless bodies such as Mars that have not been subsequently modified by volcanism are "saturated" in craters. Craters appear across the entire surface of Mars, and they are vital to understanding its crustal properties as well as surface ages and modification events. They allow inferences into the ancient climate and hydrologic history, and they add a key data point for the understanding of impact physics. This study, created by Stuart Robbins, presents a new global database for Mars that contains 378,540 craters statistically complete for diameters D ≥ 1 km.

Unique Identifier: CRATER_ID

Acknowledgements

Stuart Robbins, one who created this dataset.

Inspiration

Elon Musk's mission to Mars.

NOTE FOR WMT PARTICIPANTS:There is an easier version for MT available in Moses format (one sentence per line. The files start with moses_like.If you use this dataset, please cite the following wordk:@InProceedings{L18-1546, author = "Soares, Felipe and Moreira, Viviane and Becker, Karin", title = "A Large Parallel Corpus of Full-Text Scientific Articles", booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)", year = "2018", publisher = "European Language Resource Association", location = "Miyazaki, Japan", url = "http://aclweb.org/anthology/L18-1546" }We developed a parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences

The data and programs replicate tables and figures from "A Large Canadian Database for Macroeconomic Analysis", by Fortin-Gagnon, Leroux, Stevanovic and Surprenant. Please see the ReadMe file for additional details.

CHCP Overview:The human behavior and brain are shaped by genetic, environmental and cultural interactions. Recent advances in neuroimaging integrate multimodal imaging data from a large population and start to explore the large-scale structural and functional connectomic architectures of the human brain. One of the major pioneers is the Human Connectome Project (HCP) that developed sophisticated imaging protocols and has built a collection of high-quality multimodal neuroimaging, behavioral and genetic data from US population. A large-scale neuroimaging project parallel to the HCP, but with a focus on the East Asian population, will allow comparisons of brain-behavior associations across different ethnicities and cultures. The Chinese Human Connectome Project (CHCP) is launched in 2017 and led by Professor Jia-Hong GAO at Peking University, Beijing, China. CHCP aims to provide large sets of multimodal neuroimaging, behavioral and genetic data on the Chinese population that are comparable to the data of the HCP. The CHCP protocols were almost identical to those of the HCP, including the procedure for 3T MRI scanning, the data acquisition parameters, and the task paradigms for functional brain imaging. The CHCP also collected behavioral and genetic data that were compatible with the HCP dataset. The first public release of the CHCP dataset is in 2022. CHCP dataset includes high-resolution structural MR images (T1W and T2W), resting-state fMRI (rfMRI), task fMRI (tfMRI), and high angular resolution diffusion MR images (dMRI) of the human brain as well as behavioral data based on Chinese population. The unprocessed "raw" images of CHCP dataset (about 1.85 TB) have been released on the platform and can be downloaded. Considering our current cloud-storage service, sharing full preprocessed images (up to 70 TB) requires further construction. We will be actively cooperating with researchers who contact us for academic request, offering case-by-case solution to access the preprocessed data in a timely manner, such as by mailing hard disks or a third-party trusted cloud-storage service. V2 Release (Date: January 16, 2023):Here, we released the seven major domains task fMRI EVs files, including: 1) visual, motion, somatosensory, and motor systems; 2) category specific representations; 3) working memory/cognitive control systems; 4) language processing (semantic and phonological processing); 5) social cognition (Theory of Mind); 6) relational processing; and 7) emotion processing.V3 Release (Date: January 12, 2024):This version of data release primarily discloses the CHCP raw MRI dataset that underwent “HCP minimal preprocessing pipeline”, located in CHCP_ScienceDB_preproc folder (about 6.90 TB). In this folder, preprocessed MRI data includes T1W, T2W, rfMRI, tfMRI, and dMRI modalities for all young adulthood participants, as well as partial results for middle-aged and older adulthood participants in the CHCP dataset. Following the data sharing strategy of the HCP, we have eliminated some redundant preprocessed data, resulting in a final total size of the preprocessed CHCP dataset is about 6.90 TB in zip files. V4 Release (Date: December 4, 2024):In this update, we have fixed the issue with the corrupted compressed file of preprocessed data for subject 3011, and removed the incorrect preprocessed results for subject 3090. Additionally, we have updated the subject file information list. Additionally, this release includes the update of unprocessed "raw" images of the CHCP dataset in CHCP_ScienceDB_unpreproc folder (about 1.85 TB), addressing the previously insufficient anonymization of T1W and T2W modalities data for some older adulthood participants in versions V1 and V2. For more detailed information, please refer to the data descriptions in versions V1 and V2.CHCP Summary:Subjects:366 healthy adults (Chinese Han)Imaging Scanner:3T MR (Siemens Prisma)Institution:Peking University, Beijing, ChinaFunding Agencies:Beijing Municipal Science & Technology CommissionChinese Institute for Brain Research (Beijing)National Natural Science Foundation of ChinaMinistry of Science and Technology of China CHCP Citations:Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from CHCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Chinese Human Connectome Project (CHCP, PI: Jia-Hong Gao) funded by the Beijing Municipal Science & Technology Commission, Chinese Institute for Brain Research (Beijing), National Natural Science Foundation of China, and the Ministry of Science and Technology of China."

Measuring the completeness of survey inventories created by citizen-science initiatives can identify the strengths and shortfalls in our knowledge of where species occur geographically. Here, we use occurrence information from eBird to measure the survey completeness of the world’s birds in this database at three temporal resolutions and four spatial resolutions across the annual cycle during the period 2002 to 2018. Approximately 84% of the earth’s terrestrial surface contained bird occurrence information with the greatest concentrations occurring in North America, Europe, India, Australia, and New Zealand. The largest regions with low levels of survey completeness were located in central South America, northern and central Africa, and northern Asia. Across spatial and temporal resolutions, survey completeness in regions with occurrence information was 55–74% on average, with the highest values occurring at coarser temporal and coarser spatial resolutions and during spring migration wi...