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.

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.

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

Database survey. Visit https://dataone.org/datasets/sha256%3Ac123c47552f18a3889402bcbb67b97839d82605e35b9f70b2e07da65b8dcc3e9 for complete metadata about this dataset.

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.

Context

The Iris database was the first DB I used at the beginning of my career as a data science student!

Content

This DB was introduced to me by my professor John Ponciano from Samsung Ocean

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

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.

Journal of Chemistry Impact Factor 2025-2026 - ResearchHelpDesk - Journal of Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles on all aspects of fundamental and applied chemistry. Journal of Chemistry is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Journal of Chemistry is included in many leading abstracting and indexing databases. For a complete list, click here. The most recent Impact Factor for Journal of Chemistry is 1.727 according to the 2018 Journal Citation Reports released by Clarivate Analytics in 2019. The journal’s most recent CiteScore is 1.32 according to the CiteScore 2018 metrics released by Scopus. Abstracting and Indexing Academic Search Alumni Edition Academic Search Complete AgBiotech Net AgBiotech News and Information AGRICOLA Agricultural Economics Database Agricultural Engineering Abstracts Agroforestry Abstracts Animal Breeding Abstracts Animal Science Database Biofuels Abstracts Botanical Pesticides CAB Abstracts Chemical Abstracts Service (CAS) CNKI Scholar Crop Physiology Abstracts Crop Science Database Directory of Open Access Journals (DOAJ) EBSCOhost Connection EBSCOhost Research Databases Elsevier BIOBASE - Current Awareness in Biological Sciences (CABS) EMBIOlogy Energy and Power Source Global Health Google Scholar J-Gate Portal Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index Reaxys Science Citation Index Expanded Scopus Textile Technology Index The Summon Service WorldCat Discovery Services

No description was included in this Dataset collected from the OSF

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

EmoV-DB

See also

https://github.com/noetits/ICE-Talk for controllable TTS

How to use

Download link

Sorted version (recommended), new link: https://openslr.org/115/

old link (slow download) but gives ou the folder structure needed to use "load_emov_db()" function: https://mega.nz/#F!KBp32apT!gLIgyWf9iQ-yqnWFUFuUHg

Not sorted version: http://www.coe.neu.edu/Research/AClab/Speech%20Data/

Forced alignments with gentle

"It is the process of taking the text transcription of an audio speech segment and determining where in time particular words occur in the speech segment." source

It also allows to separate verbal and non-verbal vocalizations (laughs, yawns, etc.)

1. Go to https://github.com/lowerquality/gentle
2. Clone the repo
3. In Getting started, use the 3rd option: .\install.sh
4. Copy align_db.py in the repository
5. In align_db.py, change the "path" variable so that it corresponds to the path of EmoV-DB.
6. Launch command "python align_db.py". You'll probably have to install some packages to make it work

7. It should create a folder called "alignments" in the repo, with the same structure as the database, containing a json file for each sentence of the database.

8. The function "get_start_end_from_json(path)" allows you to extract start and end of the computed force alignment

9. you can play a file with function "play(path)"

10. you can play the part of the file in which there is speech according to the forced alignment with "play_start_end(path, start, end)"

Overview of data

The Emotional Voices Database: Towards Controlling the Emotional Expressiveness in Voice Generation Systems

• This dataset is built for the purpose of emotional speech synthesis. The transcript were based on the CMU arctic database: http://www.festvox.org/cmu_arctic/cmuarctic.data.

• It includes recordings for four speakers- two males and two females.

• The emotional styles are neutral, sleepiness, anger, disgust and amused.

• Each audio file is recorded in 16bits .wav format

• Spk-Je (Female, English: Neutral(417 files), Amused(222 files), Angry(523 files), Sleepy(466 files), Disgust(189 files))

• Spk-Bea (Female, English: Neutral(373 files), Amused(309 files), Angry(317 files), Sleepy(520 files), Disgust(347 files))

• Spk-Sa (Male, English: Neutral(493 files), Amused(501 files), Angry(468 files), Sleepy(495 files), Disgust(497 files))

• Spk-Jsh (Male, English: Neutral(302 files), Amused(298 files), Sleepy(263 files))

• File naming (audio_folder): anger_1-28_0011.wav - 1) first word (emotion style), 1-28 - annotation doc file range, Last four digit is the sentence number.

• File naming (annotation_folder): anger_1-28.TextGrid - 1) first word (emotional style), 1-28- annotation doc range

References

A description of the database here: https://arxiv.org/pdf/1806.09514.pdf

Please reference this paper when using this database:

Bibtex: @article{adigwe2018emotional, title={The emotional voices database: Towards controlling the emotion dimension in voice generation systems}, author={Adigwe, Adaeze and Tits, No{\'e} and Haddad, Kevin El and Ostadabbas, Sarah and Dutoit, Thierry}, journal={arXiv preprint arXiv:1806.09514}, year={2018} }

Data for: “STEM: Scientific Database for Vocational Education and Training for Secondary Level" published by RAC-Revista de Administração Contemporânea. Technical-scientific database for technical courses that enables standardized analyses and comparisons for educational research and planning, aiming to expand knowledge about STEM (Science, Technology, Engineering, and Mathematics) areas in Vocational education and training (VET) in Brazil.

In the Great Lakes basin, there are numerous organizations undertaking scientific monitoring and research efforts with the goal of identifying threats and evaluating management strategies that will protect and restore the Great Lakes ecosystem. Coordination among all these stakeholders is a challenge, and having a centralized location where researchers and managers can identify relevant scientific activities and access fundamental information about these activities is crucial for efficient management. The Science in the Great Lakes (SiGL) Mapper was a map-based discovery tool that spatially displayed basin-wide multidisciplinary monitoring and research activities conducted by both USGS and partners from all five Great Lakes. It was designed to help Great Lakes researchers and managers strategically plan, implement, and analyze monitoring and restoration activities by providing easy access to historical and on-going project metadata while allowing them to identify gaps (spatially and topically) that have been underrepresented in previous efforts or need further study. SiGL provided a user-friendly and efficient way to explore Great Lakes projects and data through robust search options while also providing a critical spatial perspective through its interactive mapping interface.

Gms-index-mediator is a standalone index for spatio-temporal data acting as a mediator between an application and a database. Even modern databases need several minutes to execute a spatio-temporal query to huge tables containing several million entries. Our index-mediator speeds the execution of such queries up by several magnitues, resulting in response times around 100ms. This version is tailored towards the GeoMultiSens database, but can be adapted to work with custom table layouts with reasonable effort.

The Co-Creation Database groups scientific references on co-creation.

It mainly contains the title, abstract, DOI, and authors.

Two versions are available:

• Version 1.5 includes 13,501 references, from PubMed, ProQuest and CINAHL, from January 1970 to November 2021. Available in RIS (Research Information Systems) format and CSV (CSV UTF-8). Quality metrics: 9.38% false negatives; 20.35% false positives.
• Version 2.0 is an update from a classification model trained with version 1.5. It includes references from Scopus and Web of Science from January 1970 to March 2023, with an update of the previous databases used for version 1.5 from December 2021 to March 2023. Two CSV (CSV UTF-8) files are available. The "Co-Creation Database v2.0 - full.csv" combines the last version, 1.5 and the update, with 52,821 references. The file "Co-Creation Database v2.0 - adding.csv" has only the update, with 39,219 references. Quality metrics: 13.98% false negatives; 36.43% false positives.

To perform your search: we recommend you extend your search to the title and abstract since some data are initially missing. The RIS file can be uploaded to any references manager (e.g., Zotero, Mendeley, etc.), where you will have the feature to search. For example, here is the link for advanced search instructions in Zotero: https://www.zotero.org/support/searching. Additionally, You can run a Boolean search for CSV files using a Python script.

To improve the database in further updates: we make available an online form to submit any irrelevant references you may find or to submit any relevant reference not inside the last version. The form is available at the following link: https://forms.office.com/e/6vu9X0kBcw

It was produced as part of Health CASCADE, a Marie Skłodowska-Curie Innovative Training Network funded by the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement n° 956501.

The work is made available under the terms of the license CC-BY-NC-4.0 (Creative Common Attribution - NonCommercial - NoDerivatives 4.0 International).

This database is the result of an extensive literature search aimed at identifying documents relevant to the emerging field of dam removal science. In total the database contains 296 citations that contain empirical monitoring information associated with 207 different dam removals across the United States and abroad. Data includes publications through 2020 and supplemented with the U.S. Army Corps of Engineers National Inventory of Dams database, U.S. Geological Survey National Water Information System and aerial photos to estimate locations when coordinates were not provided. Publications were located using the Web of Science, Google Scholar, and Clearinghouse for Dam Removal Information.

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.

MPDS is a curated inorganic materials dataset with 30 years of research, linking phase diagrams, nanostructures, and properties for advanced materials science