This dataset is about stocks. It has 1 row and is filtered where the company is OTC Markets. It features 8 columns including stock name, company, exchange, and exchange symbol.

Bitcoin Alpha trust weighted signed network

https://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html

Dataset information

This is who-trusts-whom network of people who trade using Bitcoin on a
platform called Bitcoin Alpha (http://www.btcalpha.com/). Since Bitcoin
users are anonymous, there is a need to maintain a record of users'
reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to
+10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

Dataset statistics
Nodes 3,783
Edges 24,186
Range of edge weight -10 to +10
Percentage of positive edges 93%

Similar network from another Bitcoin platform, Bitcoin OTC, is available at https://snap.stanford.edu/data/soc-sign-bitcoinotc.html (and as
SNAP/bitcoin-otc in the SuiteSparse Matrix Collection).

Source (citation) Please cite the following paper if you use this dataset: S. Kumar, F. Spezzano, V.S. Subrahmanian, C. Faloutsos. Edge Weight
Prediction in Weighted Signed Networks. IEEE International Conference on
Data Mining (ICDM), 2016.
http://cs.stanford.edu/~srijan/pubs/wsn-icdm16.pdf

The following BibTeX citation can be used:
@inproceedings{kumar2016edge,
title={Edge weight prediction in weighted signed networks},
author={Kumar, Srijan and Spezzano, Francesca and
Subrahmanian, VS and Faloutsos, Christos},
booktitle={Data Mining (ICDM), 2016 IEEE 16th Intl. Conf. on},
pages={221--230},
year={2016},
organization={IEEE}
}

The project webpage for this paper, along with its code to calculate two
signed network metrics---fairness and goodness---is available at
http://cs.umd.edu/~srijan/wsn/

Files
File Description
soc-sign-bitcoinalpha.csv.gz
Weighted Signed Directed Bitcoin Alpha web of trust network

Data format
Each line has one rating with the following format:

SOURCE, TARGET, RATING, TIME                      

where

SOURCE: node id of source, i.e., rater                 
TARGET: node id of target, i.e., ratee                 
RATING: the source's rating for the target,              
    ranging from -10 to +10 in steps of 1             
TIME: the time of the rating, measured as seconds since Epoch.     

Notes on inclusion into the Suite...

View the OTC Markets Group Dataset providing trade data, and company and security information to suit your trading, investment, legal and regulatory needs.

This dataset is about stocks. It has 8,129 rows and is filtered where the exchange is OTC Markets Group. It features 8 columns including stock name, company, exchange, and exchange symbol.

Collaborations and deceptions in strategic interactions revealed by hyperscanning fMRI

Aims:

The current study aims to investigate the neural mechanisms of interpersonal collaborations and deceptions, with an Opening Treasure Chest (OTC) game under the fMRI hyperscanning setup.

Methods

fMRI: In this hyperscanning fMRI study, the participant pairs (n=33) from Taipei and Tainan joined an opening-treasure-chest (OTC) game, where the dyads took alternative turns as senders (to inform) and receivers (to decide) for guessing the right chest. The cooperation condition was achieved by, upon successful guessing, splitting the $200NTD trial reward, thereby promoting mutual trust. The competition condition, in contrast, was done by, also upon winning, the latter receivers taking all the $150NTD reward, thereby encouraging strategic interactions.

General findings and importance:

For fMRI, the GLM contrasts reaffirmed the three documented sub-networks related to social deception: theory-of-mind (ToM), executive control, and reward processing. Another key finding was the negative correlations between the connectivity of right temporo-parietal junction (rTPJ, known as the ToM region) and emotion-related regions, including amygdala, parahippocampal gyrus, and rostral anterior cingulate (rACC), and senders’ lying rates. Furthermore, the Multi-Voxel Pattern Analysis (MVPA) over multiple searchlight-unearthed Region-Of-Interests (ROIs) in classifying either the “truth-telling vs. lying in $150” or the “truthful in $200 vs. truthful in $150” conditions achieved 61% and 84.5%, respectively. Lastly, principal component analysis (PCA) could reduce these high dimensional fMRI data in above-mentioned comparisons to the same level of accuracy with less than 200 or less than 10 components, respectively, suggesting that it may be due more to the individual difference in explaining the suboptimal results. To sum up, these results reveal the neural substrates underpinning the idiosyncratic social deceptions in dyadic interactions.

Sample Size

Sixty-six (33 pairs) participants, between 20 and 30 years of age (M=23.4, SD=2.9), participated in the study.

Comments added by Openfmri Curators

===========================================

General Comments

Where to discuss the dataset

1) www.openfmri.org/dataset/ds******/ See the comments section at the bottom of the dataset page.

Known Issues

Bids-validator Output

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. Above-ground plant material was harvested in July (PSC) and Oct. in five years of CO2 enrichment in Open-top-chambers. There was a consistent increase in plant productivity in the elevated CO2 chambers. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=154 Webpage with information and links to data files for download

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. Every month, during the growing season, from 1997-2001, 10 small quadrats were placed in ambient and elevated CO2 open-top-chambers, and plant cover, by species, was visually estimated. In general, elevated CO2 caused an increase in one C3 grass species, Stipa comata, and a small increase in forbs. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=156 Webpage with information and links to data files for download

This dataset provides detailed information on various drugs used for a multitude of medical conditions such as acne, cancer, and heart disease. It includes essential details about drug efficacy based on user ratings and experiences, as well as specific information on side effects. The dataset aims to offer insights into how different medications are perceived by users concerning their effectiveness, considering both positive and adverse effects.

Columns

• drug_name: The name of the drug.
• medical_condition: The name of the medical condition the drug is used for. Examples include Pain (defined as "An unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage") and Cold Symptoms (also known as Common Cold or Coryza, characterised by congestion of the nasal mucous membrane, watery nasal rhinorrhoea, and general malaise, with a typical duration of 3–5 days).
• medical_condition_description: A detailed description of the medical condition.
• activity: An indicator of recent site visitor activity relative to other medications listed, based on data gathered from drugs.com.
• rx_otc: Indicates the drug's classification:
  • Rx: Prescription Needed.
  • OTC: Over-the-counter, meaning it can be purchased without a medical prescription.
  • Rx/OTC: Can be either prescription or over-the-counter.
• pregnancy_category: Classifies the drug's risk to a foetus during pregnancy:
  • A: Adequate and well-controlled studies show no risk in the first trimester (and no evidence of risk later).
  • B: Animal reproduction studies show no foetal risk, but no adequate human studies exist.
  • C: Animal studies show adverse foetal effects, no adequate human studies, but potential benefits may warrant use despite risks.
  • D: Positive evidence of human foetal risk, but potential benefits may warrant use despite risks.
  • X: Studies show foetal abnormalities and human foetal risk, risks clearly outweigh potential benefits.
  • N: FDA has not classified the drug.
• csa: Controlled Substances Act (CSA) Schedule:
  • M: Multiple schedules, depends on dosage/strength.
  • U: Schedule unknown.
  • N: Not subject to CSA.
  • 1: High abuse potential, no accepted medical use, lack of accepted safety.
  • 2: High abuse potential, accepted medical use (with severe restrictions), abuse may lead to severe dependence.
  • 3: Lower abuse potential than 1 & 2, accepted medical use, abuse may lead to moderate/low physical or high psychological dependence.
  • 4: Low abuse potential relative to 3, accepted medical use, abuse may lead to limited physical or psychological dependence.
  • 5: Low abuse potential relative to 4, accepted medical use, abuse may lead to limited physical or psychological dependence.
• alcohol: Indicates interaction with alcohol (X = Interacts with Alcohol).
• rating: User-assigned rating (1 = not effective, 10 = most effective), reflecting effectiveness, side effects, and ease of use. Ratings range from 0.00 to 10.00.
• no_of_reviews: The number of reviews received for the drug. There are 2912 unique values for this column.

Distribution

The dataset is typically provided in a CSV file format. While specific total row/record counts are not explicitly stated, the presence of 2912 unique review counts and a wide range of ratings suggest a substantial number of entries. The data appears to be structured in a tabular manner.

Usage

This dataset is ideal for: * Analysing drug efficacy based on real-world user feedback. * Researching user experiences with various medications. * Developing applications related to health information systems. * Performing Natural Language Processing (NLP) on drug descriptions and reviews to extract insights. * Understanding the landscape of prescription (Rx) versus over-the-counter (OTC) medications.

Coverage

The dataset's coverage is global, making it relevant for a worldwide audience. It was listed on 11th June 2025. There are no specific notes on demographic scope or data availability for certain groups or years explicitly mentioned.

License

CCO

Who Can Use It

This dataset is suitable for: * Healthcare Professionals: To gain insights into patient experiences and drug effectiveness. * Researchers: For studies on pharmacology, public health, and patient outcomes. * Data Analysts: To identify trends and patterns in drug usage and side effects. * Software Developers: For building health-related applications, AI models, or recommendation systems. * Patients/Consumers: To inform decisions about medications based on aggregated user experiences.

Dataset Name Suggestions

• Drug Efficacy and User Experience Data
• Medication Ratings and Conditions Dataset

soc-RedditHyperlinks: Social Network: Reddit Hyperlink Network

The hyperlink network represents the directed connections between two subreddits (a subreddit is a community on Reddit). We also provide subreddit embeddings. The network is extracted from publicly available Reddit data of 2.5 years from Jan 2014 to April 2017.

Subreddit Hyperlink Network: the subreddit-to-subreddit hyperlink network is extracted from the posts that create hyperlinks from one subreddit to another. We say a hyperlink originates from a post in the source community and links to a post in the target community. Each hyperlink is annotated with three properties: the timestamp, the sentiment of the source community post towards the target community post, and the text property vector of the source post. The network is directed, signed, temporal, and attributed.

Note that each post has a title and a body. The hyperlink can be present in either the title of the post or in the body. Therefore, we provide one network file for each.

Subreddit Embeddings: We have also provided embedding vectors representing each subreddit. These can be found in this dataset link: subreddit embedding dataset. Please note that some subreddit embeddings could not be generated, so this file has 51,278 embeddings.

soc-sign-bitcoin-otc: Bitcoin OTC trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-bitcoin-alpha: Bitcoin Alpha trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin Alpha. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-epinions: Epinions social network

This is who-trust-whom online social network of a a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user.

wiki-Elec: Wikipedia adminship election data

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. A small part of Wikipedia contributors are administrators, who are users with access to additional technical features that aid in maintenance. In order for a user to become an administrator a Request for adminship (RfA) is issued and the Wikipedia community via a public discussion or a vote decides who to promote to adminship. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all administrator elections and vote history data. This gave us nearly 2,800 elections with around 100,000 total votes and about 7,000 users participating in the elections (either casting a vote or being voted on). Out of these 1,200 elections resulted in a successful promotion, while about 1,500 elections did not result in the promotion. About half of the votes in the dataset are by existing admins, while the other half comes from ordinary Wikipedia users.

Dataset has the following format:

• E: did the elector result in promotion (1) or not (0)
• T: time election was closed
• U: user id (and screen name) of editor that is being considered for promotion
• N: user id (and screen name) of the nominator
• V: vote(1:support, 0:neutral, -1:oppose) user_id time screen_name

wiki-RfA: Wikipedia Requests for Adminship (with text)

For a Wikipedia editor to become an administrator, a request for adminship (RfA) must be submitted, either by the candidate or by another community member. Subsequently, any Wikipedia member may cast a supporting, neutral, or opposing vote.

We crawled and parsed all votes since the adoption of the RfA process in 2003 through May 2013. The dataset contains 11,381 users (voters and votees) forming 189,004 distinct voter/votee pairs, for a total of 198,275 votes (this is larger than the number of distinct voter/votee pairs because, if the same user ran for election several times, the same voter/votee pair may contribute several votes).

This induces a directed, signed network in which nodes represent Wikipedia members and edges represent votes. In this sense, the...

Japan's main stock market index, the JP225, fell to 39432 points on July 14, 2025, losing 0.35% from the previous session. Over the past month, the index has climbed 2.93%, though it remains 4.47% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. Above-ground plant material was harvested, by species, in July (PSC) in five years from ambient and elevated CO2 Open-top-chambers, and unchambered controls. There was a small difference in species composition, in the plots, in 1996; prior to any CO2 treatment; this data should be used as a covariate in looking at subsequent years. There was a consistent increase in plant productivity in the elevated CO2 chambers, primarily in the C3 grass group. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=165 Webpage with information and links to data files for download

TaxiBJ consists of trajectory data from taxicab GPS data and meteorology data in Beijing from four time intervals: 1st Jul. 2013 - 30th Otc. 2013, 1st Mar. 2014 - 30th Jun. 2014, 1st Mar. 2015 - 30th Jun. 2015, 1st Nov. 2015 - 10th Apr. 2016.

The purpose of this study is to determine if taking Vyvanse with Prilosec OTC or Adderall XR with Prilosec OTC changes how quickly the drug is absorbed into the body and/or changes how much of the drug is absorbed into the body.

This dataset provides detailed information on various drugs, including their common uses, reported side effects, and associated medical conditions. It also includes user ratings and reviews, brand names, and regulatory classifications, offering a valuable resource for understanding pharmaceutical products and their impact on health. The data covers drugs used for a range of conditions such as Acne, Cancer, and Heart Disease.

Columns

• drug_name: The common name of the drug.
• medical_condition: The medical condition for which the drug is prescribed or used.
• side_effects: A list of reported side effects associated with the drug.
• generic_name: The chemical name of the drug, distinct from its brand name.
• drug_classes: The classification of the drug based on its chemical structure, mechanism of action, or therapeutic use.
• brand_names: The commercial names under which the drug is marketed.
• activity: An indicator of recent site visitor interest relative to other medications in the list.
• rx_otc: Details whether the drug requires a prescription (Rx), is available over-the-counter (OTC), or can be either.
• pregnancy_category: A classification indicating the potential risk of the drug to a foetus during pregnancy (categories A, B, C, D, X, N).
  • A: Adequate and well-controlled studies show no risk to the foetus in the first trimester.
  • B: Animal studies show no foetal risk, but no adequate human studies exist.
  • C: Animal studies show adverse foetal effects; no adequate human studies, but potential benefits may warrant use.
  • D: Positive evidence of human foetal risk, but potential benefits may warrant use despite risks.
  • X: Studies show foetal abnormalities; risks clearly outweigh potential benefits in pregnant women.
  • N: FDA has not classified the drug.
• csa: Controlled Substances Act (CSA) Schedule, indicating the drug's potential for abuse and accepted medical use in the United States (M, U, N, 1, 2, 3, 4, 5).
  • M: Multiple possible schedules depending on dosage or strength.
  • U: Schedule is unknown.
  • N: Not subject to the Controlled Substances Act.
  • 1: High potential for abuse, no accepted medical use.
  • 2: High potential for abuse, accepted medical use with severe restrictions.
  • 3: Lower abuse potential than schedules 1 and 2, accepted medical use.
  • 4: Low abuse potential relative to schedule 3, accepted medical use.
  • 5: Low abuse potential relative to schedule 4, accepted medical use.
• alcohol: Indicates if the drug interacts with alcohol.
• rating: User-assigned effectiveness rating, typically on a scale of 1 to 10 (1 = not effective, 10 = most effective).

Distribution

The dataset is typically provided in a CSV file format. Specific details regarding the exact number of rows or records are not currently available in the provided information. A sample file will be updated separately to the platform.

Usage

This dataset is ideal for: * Pharmaceutical research: Analysing drug properties, side effects, and interactions. * Healthcare applications: Developing tools for patients and professionals to access drug information. * Public health studies: Investigating patterns of drug usage, side effect prevalence, and medication effectiveness. * Educational purposes: Supporting studies in pharmacology, medicine, and public health. * Data analytics: Building models for drug efficacy, safety monitoring, and market analysis.

Coverage

The data provides global coverage of drug information. It was listed as of 05/06/2025. The information on drugs, side effects, and medical conditions is relevant across various populations, with specific details such as pregnancy categories and controlled substance classifications reflecting regulatory and safety considerations.

License

CCO

Who Can Use It

• Researchers in pharmacology, epidemiology, and public health.
• Healthcare professionals seeking quick reference for drug details, side effects, and regulatory statuses.
• Data scientists and analysts developing health-related applications or conducting market research.
• Academics and students for educational and research projects.
• Patients and the general public interested in understanding medications they use.

Dataset Name Suggestions

• Global Drug Information Database
• Pharmaceutical Drugs and Side Effects Data
• Medication Details and Patient Reviews
• Drug Efficacy and Safety Dataset
• Healthcare Drug Reference

Attributes

Original Data Source: Drugs, Side Effects and Medical Condition

The data set presented here represents growing season (late-1999, 2000 and 2001) values of net carbon dioxide exchange, photosynthesis, and respiration between the atmosphere and wet tundra. The site is located at Alexandra Fiord on the east-central side of Ellesmere Island, Nunavut, Canada at 78.54N 75.55W, 50 m elevation asl. As part of the International Tundra Experiment (ITEX), Greg Henry of the University of British Columbia has been increasing air and soil temperatures at this site since 1993 using small open-topped chambers (OTCs). These OTCs are hexagonal in shape and are constructed of transparent fiberglass. They typically raise air and soil temperatures by 1 to 4C. The data set shows periodic carbon dioxide exchange data from the end of the 1999 growing season and throughout the 2000 and 2001 growing seasons in ambient plots and long-term (7-9 yrs) warmed (OTC) plots. The most complete data set (2001) begins in spring before winter snowmelt and ends at the end of summer when snow has once again covered the ground. The data are from 199908040800 to 200108042400 UTC.

This dataset provides the annual voluntary carbon market transaction volume, value, and price for total traded carbon credits. In addition, it provides the cumulative issuances and retirements.As source mentioned, These data on voluntary carbon market dynamics come from EM’s database of voluntarily disclosed over-the-counter (OTC) carbon credit transactions, which are shared with EM by an international network of more than 180 “EM Respondents,” including project developers, investors, and intermediaries with headquarters in over 40 countries and representing carbon credit sales from thousands of nature-based and technological carbon projects in over 100 countries.Data on project registrations, credit issuances, and retirements come from the following project registries: ACR, CAR, CDM, City Forest Credits, Global Carbon Council, Gold Standard, Plan Vivo, and VCS.

This dataset contains data on vascular plant, lichen and bryophyte functional traits and abundance from a warming experiment using open top chambers in Finse, south-west Norway.