Description

This dataset includes spectral acceleration measurements for earthquake records with a closest distance to fault (Rrup) of less than 20 km. The data is sourced from the Pacific Earthquake Engineering Research Center (PEER) NGA-West2 database, providing high-resolution spectral acceleration (pSa) values across various periods.

Key Features:

• Magnitude Range: Includes records from a broad range of magnitudes.
• Distance to Fault (Rrup): Earthquakes with Rrup ranging from 0 to 20 km.
• Vs30 (Site Velocity): Data is not constrained by Vs30 values.
• Damping Ratio: Standard 5% damping used.
• Scaling Method: No scaling applied to the data.
• Period Array: Data includes spectral acceleration values across a range of periods.
• Fault Type: Covers all fault types.
• Scaling Method: No scaling applied; data is presented as recorded.

Citation:

For more information on the dataset and its sources, please refer to the PEER NGA-West2 database.

Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, sear- ching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and algorithms for P2P environments. Next it discusses some of the privacy concerns with P2P data mining and points out the problems of existing privacy-preserving multi-party data mining techniques. It further points out that most of the nice assumptions of these existing privacy preserving techniques fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). The paper offers a more realistic formulation of the PPDM problem as a multi-party game and points out some recent results.

This provides a link to the version of the Pulse Wave Database which was made available through PhysioNet for the purposes of peer review.This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It will contain pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database will contain a baseline set of pulse waves for each of the six age groups, which was created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It will also contain 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive in silico analyses of the performance of pulse wave analysis algorithms.

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science. The data include journals that are open access, which was first defined by the Budapest Open Access Initiative: By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in English or abstracted in English, 3) actively published at the time of..., Data Collection In the spring of 2023, researchers gathered 377 scholarly journals whose content covered the work of librarians, archivists, and affiliated information professionals. This data encompassed 221 journals from the Proquest database Library and Information Science Abstracts (LISA), widely regarded as an authoritative database in the field of librarianship. From the Directory of Open Access Journals, we included 144 LIS journals. We also included 12 other journals not indexed in DOAJ or LISA, based on the researchers’ knowledge of existing OA library journals. The data is separated into several different sets representing the different indices and journals we searched. The first set includes journals from the database LISA. The following fields are in this dataset:

Journal: title of the journal

Publisher: title of the publishing company

Open Data Policy: lists whether an open data exists and what the policy is

Country of publication: country where the journal is publ..., , # Open access practices of selected library science journals

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science.

The data include journals that are open access, which was first defined by the Budapest Open Access Initiative:Â

By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.

Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in Engli...

This csv file contains a descriptive dataset of 617 scholarly journals that make use of a form of Open Peer Review (OPR) based on Open Reports and/or Open Reviewer Identities. The data file contains the following fields:

Journal Title

Year of First Identified OPR Occurrence (2001-2019)

High Level Discipline of the Journal (Humanities, Medical and Health Sciences, Multidisciplinary, Natural Sciences, Social Sciences, Technology)

Journal URL

Journal Publisher

Publisher Country

Use of Open Reports (Decided by Author, Decided by Editor, Mandated by Journal, None)

Use of Open Reviewer Identities (Decided by Reviewer, Mandated, None)

Notes that provide additional information about the journal

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

Payrolled businesses, employment concentration, jobs, percent change, competitive effect, GDP per strategic industry cluster and city.

PER-Base contains information on institutional research evaluation in the Netherlands. It covers results from evaluations with the 'Vereniging van Samenwerkende Nederlandse Universiteiten' - VSNU 1993, VSNU 1994 and VSNU 1998 protocols as well as the 'Standard Evaluation Protocol' - SEP 2003-2009 and SEP 2009-2015 protocols. The information in the database is derived from the 222 known evaluation reports: protocol used for the evaluation, title of the evaluation report, year of publication of the evaluation report, organisations involved, programs involved, score per criterion per program. The 'Hoger Onderwijs en Onderzoek Plan' - HOOP codes (discipline) are allocated by the authors. PER-Base is developed in 2010-2012 by the 'Center for Higher Education Policy Studies'- CHEPS - University of Twente. The Dutch Ministry of Education, Culture and Science has paid for the development as part of the CHERPA project. In 2012 the database has been transferred to the Rathenau Institute, that will maintain the database.

Much literature on peer influence has relied on central tendency– based approaches to examine the role of peer groups. This article develops a distributional framework that (1) differentiates between the influence of depressive peers and that of a majority group of non-depressive peers; and (2) considers the multilayered nature of peer environments. The authors investigate which segments of the distribution of peer depressive symptoms drive peer effects on adolescent depression across different layers of peer groups. Results from the Add Health data show that, for institutionally imposed peer groups, exposure to depressive peers significantly increases adolescents’ depressive symptoms. For self-selected peer groups, the central tendency of peer depression largely captures its impact on adolescent depression. High parent-child attachment buffers the deleterious consequence of exposure to depressive grademates. The implications of these findings are discussed for research and policy regarding peer effects on adolescent well-being.

A database that archives publications and allows users to review and critique them. Users can write comments for and rate submitted publications for references, originality, argumentation, and reliability. Papercritic also collects tweets and blog posts about published papers to add as reviews and comments. Researchers who submit their published work to PaperCritic can keep track of multiple types of feedback. All reviews must be submitted with full identity disclosure.

ReadMe File for PADDDtracker.org Data Release Version 2.1

Prepared by Conservation International, May 2021

Thank you for downloading the PADDDtracker.org Data Release Version 2.1. This dataset includes data on Protected Area Downgrading, Downsizing, and Degazettement (PADDD). Most data have been validated by peer-review, with the limited exception of newly added data from the United States and Brazil; see below for further details and links to publications.

Definitions for Protected Area Downgrading, Downsizing, and Degazettement (PADDD):

• Downgrading: A decrease in legal restrictions on the number, magnitude, or extent of human activities within a protected area by a relevant authority
• Downsizing: A decrease in the size of a protected area as a result of excision of land or sea area through a legal boundary change
• Degazettement: A loss of legal status of a protected area under government administration (national, state, provincial or local)

This data release contains data from the following peer-reviewed studies:

• M.B. Mascia, S. Pailler, Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conservation Letters. 4, 9–20 (2011). DOI: 10.1111/j.1755-263X.2010.00147.x .
• M.B. Mascia et al., Protected area downgrading, downsizing, and degazettement (PADDD) in Africa, Asia, and Latin America and the Caribbean, 1900–2010. Biological Conservation. 169, 355–361 (2014). DOI: 10.1016/j.biocon.2013.11.021 .
• J.L. Forrest et al., Tropical deforestation and carbon emissions from protected area downgrading, downsizing, and degazettement (PADDD). Conservation Letters. 8, 153–161 (2015). DOI: 10.1111/conl.12144 .
• S.M. Pack et al., Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biological Conservation. 197, 32–39 (2016). DOI: 10.1016/j.biocon.2016.02.004.
• R.E. Golden Kroner, R. Krithivasan, M.B. Mascia, Effects of protected area downsizing on habitat fragmentation in Yosemite National Park (USA), 1864 - 2014. Ecology and Society. 21 (2016). DOI: 10.5751/ES-08679-210322.
• C.N. Cook, R.S. Valkan, M.B. Mascia, M.A. McGeoch, Quantifying the extent of protected-area downgrading, downsizing, and degazettement in Australia. Conservation Biology. 31, 1039–1052 (2017). DOI: 10.1111/cobi.12904.
• R.E. Golden Kroner et al., The uncertain future of protected lands and waters. Science. 31, 364 (6443), 881-886 (2019). DOI: 10.1126/science.aau5525.
• T. Dorji, S. Linke, F. Sheldon, Half century of protected area dynamism in the country of Gross National Happiness, Bhutan. Conservation Science and Practice. (2019). DOI: 10.1111/csp2.46.
• A. De Vos, H. Clements, D. Biggs, G.S. Cumming, The dynamics of proclaimed privately protected areas in South Africa over 83 years. Conservation Letters. 12 (6) (2019). DOI: 10.1111/conl.12644.
• R. Albrecht, C.N. Cook, O. Andrews, K.E. Roberts, M.F.J. Taylor, M.B. Mascia, R.E. Golden Kroner, Protected area downgrading, downsizing, and degazettement (PADDD) in marine protected areas. Marine Policy. 129 (2021). 104437, ISSN 0308-597X. DOI: 10.1016/j.marpol.2021.104437.

Please contact paddd.team@gmail.com to request full text versions of publications if not otherwise open access.

Please note that 353 (7% of) records in the database (new records from the United States and Brazil) have not yet been validated by peer review; see Olsson et al. 2021 for more information about these data:

• Olsson, E., Albrecht, R., & Golden Kroner, R.E. (2021). PADDDtracker Data Release Version 2.1: Technical Notes. Conservation International, Arlington, VA. DOI: 10.5281/zenodo.4749615.

Differences between Version 2.1 and previous data releases:

PADDDtracker data release Version 2.1 contains 21 new fields:

• Sys_Code
• Map_Details
• Map_Source
• Notes
• Last_Update
• AddedBy
• isLatestVe
• Data_Status
• Peer_Reviewed
• Study_Link
• Off_Type
• Off_Area
• Off_Details
• Off_Source
• Rev_Type
• Rev_Area
• Rev_Details
• Rev_Source
• Legal_Type
• Marine
• Marine_ZID

Two fields included in the previous data release have been archived; these are both out-of-date ID fields that are no longer necessary to retain.

• PADDDIDOld
• GID_String

Version history

• Version 1.0 was released in January 2014, and contains 601 PADDD events from Africa, Asia, Latin America and the Caribbean, spanning from 1900 to 2012.
• Version 1.1 was released in January 2017, and contains a total of 721 PADDD events, including updated data from version 1.0 as well as additional PADDD events in the Democratic Republic of the Congo, Peru and Brazil, spanning from 1900 to 2016.
• Version 2.0 was released in May 2019, and contains a total of 4597 PADDD events, including updated PADDD events from previous data releases (1.0 and 1.1), and new records from around the world (Africa, Asia, Australia, Europe, Latin America and the Caribbean, North America).
• Version 2.1 contains a total of 4962 PADDD events, including updated data from version 2.0 as well as additional PADDD events in Australia, Brazil, Bhutan, Palau, South Africa, and the United States. It also includes several new fields for supporting details data, including for offsets and reversals. Please consult the Field Definitions PDF and/or Olsson et al. 2021 in the data release package for information on these new field names, field name definitions, data values, and clarifications.

In the folder PADDDtracker_DataReleaseV2_1_2021, you will find:

PADDDtracker_DataReleaseV2_1_2021.xlsx: this Excel file contains data on all known PADDD events, including numerical and categorical data. This includes data on location, dates, areas, IUCN categories, proximate causes and other descriptive attributes associated with PADDD events. The file contains the following tabs:

• ReadMe: introduction to dataset.
• FieldDefinitions: contains definitions for all fields (attributes) in the database.
• PADDDEvents: Dataset of known enacted and proposed PADDD events, including attributes describing location, timing, proximate causes, and associated supporting information and sources. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDReversals: information about location, dates, etc. on PADDD events with full or partial reversals for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDOffsets: information about location, dates, etc. on PADDD events with known spatial or regulatory offsets for which spatial data are available. The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.
• PADDDMPAZones: information about location, dates, zone name(s), and other descriptive information on PADDD events in marine protected areas (MPAs) in cases where MPAs were split into two or more use zones (see FieldDefinitions tab for zone domain values). The Supporting Details and References are omitted from the shapefile, as the text exceeds the character limit.

Primary GIS Datasets:

• PADDDtracker_DataReleaseV2_1_2021_Pts.shp: This shapefile contains point data for all validated PADDD events corresponding with the accompanying Excel sheet.

Please note that for an event for which the exact location is unknown, it is represented by a point placed either at the PA centroid or within the PA extent if a multipart polygon (for downgrades or downsizes), or on the capital city of the country. If using PADDD events data for spatially explicit analyses for which locations of event areas are necessary, please use the field “Location_K” as a filter to remove events with unknown locations.

• PADDDtracker_DataReleaseV2_1_2021_Poly.shp: This shapefile contains validated polygon data for PADDD events corresponding with the accompanying Excel sheet.

Supplemental GIS Datasets:

• PADDDtracker_DataReleaseV2_1_2021_Pts_Reversals.shp: Point shapefile of reversals to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_Reversals.shp: Polygon shapefile of reversals to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_Offsets.shp: Polygon shapefile of offsets to PADDD events.
• PADDDtracker_DataReleaseV2_1_2021_Poly_MPAZones.shp: Polygon shapefile of zoning changes that constitute PADDD events.

Additional Resources

• PADDDtracker Technical Guide V2, 2020: contains guidance for researchers to collect PADDD data.
• PADDDtracker Data Release 2.1 Technical Note, 2021: provides more details on PADDD Reversals,

ABSTRACT

The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.

This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: https://data2.scec.org/ugms-mcerGM-tool_v18.4/.

For more information, please see https://www.scec.org/research/ugms.

DISCLAIMER

The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.

Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.

Connection to data from federated data centres in the WOUDC Data Registry Search Index

This paper proposes a scalable, local privacy preserving algorithm for distributed Peer-to-Peer (P2P) data aggregation useful for many advanced data mining/analysis tasks such as average/sum computation, decision tree induction, feature selection, and more. Unlike most multi-party privacy-preserving data mining algorithms, this approach works in an asynchronous manner through local interactions and it is highly scalable. It particularly deals with the distributed computation of the sum of a set of numbers stored at different peers in a P2P network in the context of a P2P web mining application. The proposed optimization based privacy-preserving technique for computing the sum allows different peers to specify different privacy requirements without having to adhere to a global set of parameters for the chosen privacy model. Since distributed sum computation is a frequently used primitive, the proposed approach is likely to have significant impact on many data mining tasks such as multi-party privacy-preserving clustering, frequent itemset mining, and statistical aggregate computation.

This CBHSQ Data Brief presents findings from the evaluation of the State Targeted Response to the Opioid Crisis Grants (Opioid STR) that describe how funding was used to support peer services for people with or recovering from opioid use disorder (OUD). The evaluation revealed that most Opioid STR grantees used funds to implement, expand, or enhance peer services. The peer services most commonly provided were coaching, mentoring, and providing information and referrals to relevant services.The Opioid STR was the predecessor of the State Opioid Response (SOR) grant program and informed its development. At the start of SOR, grantees were expected to provide an array of services based on needs identified in their STR strategic plan. SOR addresses the opioid overdose crisis by providing resources to states and territories for increasing access to FDA-approved medications for the treatment of opioid use disorder (MOUD), and for supporting the continuum of prevention, harm reduction, treatment, and recovery support services.

Peer-to-Peer Data Exchange for Vehicles Market Outlook

According to our latest research, the global Peer-to-Peer Data Exchange for Vehicles market size reached USD 2.15 billion in 2024, driven by rapid advancements in connected vehicle technologies and increasing demand for real-time data sharing among vehicles and infrastructure. The market is growing at a robust CAGR of 18.7% and is forecasted to reach USD 11.38 billion by 2033. This remarkable expansion is primarily fueled by the surge in autonomous and electric vehicles, the proliferation of smart city initiatives, and the growing emphasis on road safety and traffic efficiency.

The primary growth factor for the Peer-to-Peer Data Exchange for Vehicles market is the accelerating integration of advanced connectivity solutions in modern vehicles. Automakers and technology firms are increasingly collaborating to embed Vehicle-to-Everything (V2X) capabilities, enabling seamless data exchange between vehicles, infrastructure, pedestrians, and the cloud. This connectivity is essential for supporting autonomous driving, predictive maintenance, and real-time navigation, all of which require continuous and secure data sharing. Furthermore, the rising adoption of 5G networks is amplifying the speed and reliability of these data exchanges, making peer-to-peer communication more viable and scalable across global fleets.

Another key driver is the growing focus on road safety and traffic management. Governments and regulatory bodies worldwide are implementing stringent mandates for vehicle safety, prompting the adoption of technologies that facilitate instant data sharing among vehicles and infrastructure. Peer-to-peer data exchange enables vehicles to communicate hazards, traffic conditions, and accident alerts in real-time, significantly reducing response times and enhancing overall road safety. Additionally, the integration of artificial intelligence and machine learning into data exchange platforms is allowing for more intelligent and context-aware decision-making, further propelling market growth.

The evolution of smart cities and urban mobility solutions is also playing a pivotal role in the expansion of the Peer-to-Peer Data Exchange for Vehicles market. Urban planners are increasingly leveraging connected vehicle data to optimize traffic flow, reduce congestion, and lower emissions. Peer-to-peer data exchange forms the backbone of these initiatives by providing accurate, real-time information on vehicle movements, road conditions, and pedestrian activity. Moreover, the emergence of Mobility-as-a-Service (MaaS) platforms relies heavily on robust data exchange mechanisms to coordinate multimodal transportation and enhance user experiences, thereby driving further market adoption.

The concept of a Vehicle Data Exchange Platform is becoming increasingly pivotal in the automotive industry. As vehicles become more connected, the need for a centralized platform to facilitate seamless data exchange is paramount. These platforms serve as a hub where data from various sources, such as vehicle sensors, infrastructure, and cloud services, can be aggregated, processed, and distributed in real-time. This not only enhances the functionality of connected vehicles but also supports the development of new services and applications that improve the overall driving experience. The integration of such platforms is essential for enabling advanced features like predictive maintenance and personalized infotainment, which rely heavily on accurate and timely data.

From a regional perspective, North America currently leads the market due to early adoption of connected vehicle technologies and strong government support for intelligent transportation systems. Europe follows closely, buoyed by stringent safety regulations and large-scale smart city projects. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, increasing vehicle sales, and significant investments in digital infrastructure. Latin America and the Middle East & Africa, while still emerging, are expected to experience steady growth as connectivity solutions become more accessible and affordable in these regions.

This dataset provides detailed information on links, papers, and peer reviews from the International Conference on Learning Representations (ICLR) for the years 2018 through 2023. The dataset can be used to replicate experiments or conduct new analyses on scientific reviews and decisions from OpenReview.

Content overview: - iclr_{year}_links.csv: Contains the IDs and links to the articles on OpenReview. - iclr_{year}_papers.csv: Includes the article IDs, titles, and forum identifiers (Forum) on OpenReview. - iclr_{year}_reviews.csv: Provides review data, including: - Forum: The article's unique identifier. - Type: The type of review content (e.g., title, comment, decision, rating). - Content: The text associated with each type.

The Buildings Performance Database (BPD) unlocks the power of building energy performance data. The platform enables users to perform statistical analysis on an anonymous dataset of tens of thousands of commercial and residential buildings from across the country. Users can compare performance trends among similar buildings to identify and prioritize cost-saving energy efficiency improvements and assess the range of likely savings from these improvements. Key Features - The BPD contains actual data on tens of thousands of existing buildings--not modeled data or anecdotal evidence. The BPD enables statistical analysis without revealing information about individual buildings. The BPD cleanses and validates data from many sources and translates it into a standard format. Analysis Tools - Peer Group Tool. Allows users to peruse the BPD and create peer groups based on specific building types, locations, sizes, ages, equipment and operational characteristics. Users can compare the energy use of their own building to a peer group of BPD buildings. Retrofit Analysis Tool. Allows users to analyze the savings potential of specific energy efficiency measures. Users can compare buildings that utilize one technology against peer buildings that utilize another. Coming Soon! Data Table Tool. Allows users to generate and export statistical data about peer groups. Financial Forecasting Tool. Forecasts cash flows for energy efficiency projects. Application Programming Interface (API). Allows external software to conduct analysis of the BPD data.

Collection of curated structural variation in the human genome. Catalogue of human genomic structural variation identified in healthy control samples for studies aiming to correlate genomic variation with phenotypic data. It is continuously updated with new data from peer reviewed research studies. The Database is no longer accepting direct submission of data as they are currently part of a collaboration with two new archival CNV databases at EBI and NCBI, called DGVa and dbVAR, respectively. One of the changes to DGV as part of this collaborative effort is that they will no longer be accepting direct submissions, but rather obtain the datasets from DGVa (short for DGV archive). This will ensure that the three databases are synchronized, and will allow for an official accessioning of variants.

An Excel spreadsheet which includes every peer reviewed publication written by occupational therapists in mental health since 2000. It is updated each January to include the previous years publications. Information recorded includes author number, author designation, bibliographic details (i.e. title, journal), categorisation according to doing/being/becoming/belonging, levels of evidence and days between submission and acceptance, and acceptance and publication.