The exploitation of textual unstructured content (news, company filings, earnings calls etc) in financial analysis is quickly expanding across both quantitative and discretionary strategies as demonstrated by the growing number of academic papers and products in this domain.
The Brain Language Metrics on Earnings Calls Transcripts (BLMECT) dataset has the objective of monitoring several language metrics the quarterly earnings call transcripts for 4500+ US stocks.
The dataset is made of two parts; one includes the language metrics for the most recent earnings call transcript for each stock, namely:
1) Financial sentiment
2) Percentage of words belonging to financial domain classified by language types: - “Constraining” language - “Litigious” language - “Uncertainty” language
3) Readability score
4) Lexical metrics such as lexical density and richness
5) Text statistics such as the report length and the average sentence length
The second part includes the differences between the most recent earnings call transcript and the previous one:
1) Difference of the various language metrics (e.g. delta sentiment, delta readability score delta, delta percentage of a specific language type etc.)
2) Similarity metrics between documents, also with respect to a specific language type (for example similarity with respect to “litigious” language or “uncertainty” language)
The metrics calculation is reported separately for the following sections of the transcript:
a) Management Discussion b) Analysts Questions c) Management Answers to Analysts Questions
The dataset is updated with a daily frequency since new earnings calls transcripts are published every day for some of the universe stocks. Clearly the data for each stock will change on a quarterly basis when new earnings calls are published. The historical dataset is available from year 2012.
Factsheet https://braincompany.co/assets/files/BLM_ECT_summary.pdf
Data dictionary https://braincompany.co/assets/files/BLM_ECT_data_dictionary.pdf
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
Statewide Intake serves as the “front door to the front line” for all DFPS programs. As the central point of contact for reports of abuse, neglect and exploitation of vulnerable Texans, SWI staff are available 24 hours a day, 7 days per week, 365 days per year.
SWI is the Centralized point of intake for child abuse and neglect, abuse, neglect or exploitation of people age 65 or older or adults with disabilities, clients served by DSHS or DADS employees in State Hospitals or State Supported Living Centers, and children in licensed child-care facilities or treatment centers for the entire State of Texas.
SWI provides daily reports on call volume per application; hold times per application, etc. and integrates hardware and software upgrades to phone and computer systems to reduce hold times and improve efficiency.
NOTE: Past Printed Data Books also included EBC, Re-Entry and Support Staff in all queues total.
An abandoned call is a call that disconnects after completing navigation of the recorded message, but prior to being answered by an intake specialist.
Legislative Budget Board (LBB) Performance Measure Targets are set every two years during Legislative Sessions.
LBB Average Hold Time Targets for English Queue: 2010 11.4 minutes 2011 11.4 minutes 2012 8.7 minutes 2013 8.7 minutes 2014 8.7 minutes 2015 8.7 minutes 2016 7.2 minutes 2017 10.5 minutes 2018 12.0 minutes 2019 9.8 minutes
Visit dfps.state.tx.us for information on all DFPS programs
Calls for Service to NYPD's 911 system This dataset documents entries into the NYPD 911 system, ICAD. The data is collected from the ICAD system which call takers and dispatchers use to communicate with callers and the NYPD. Each record represents an entry into the system. The data includes entries generated by members of the public as well as self-initiated entries by NYPD Members of Service. The data can be used for issues being responded to by the NYPD.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
911 Public Safety Answering Point (PSAP) service area boundaries in the United States According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such a system from within a military base, it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems, or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise, military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases, areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPs are (i.e., load balancing), operational status (i.e., redundancy), or time of day / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases, this was geospatial information; in other cases, it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g., city and county boundaries). In some cases, existing boundaries had to be modified to reflect coverage areas (e.g., "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county plus the homes and businesses along a road which is partly in another county. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.
All 311 Service Requests from 2010 to present. This information is automatically updated daily.
Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4
Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5
Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n
Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562
Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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This dataset is a combination of four years of Apple ($AAPL) options end of day quotes ranging from 01-2016 to 03-2023. Each row represents the information associated with one contract's strike price and a given expiration date.
Dates quotes are given in in Unix and in "YYYY-MM-DD HH:MM" formats. Quote frequency is daily at 4:00 pm EST, which corresponds with end of day market closure.
REMEMBER: Apple stock split on August 28, 2020. This will be reflected in the data. Keep this in mind!
What is an option chain?
An option chain can be defined as the listing of all option contracts. It comes with two different sections: call and put. A call option means a contract that gives you the right but does not give you the obligation to buy an underlying asset at a particular price and within the option's expiration date. This means that in this dataset, there will be the entire option chain (all available option contracts for all expirations) for each business day between Q1 2016 and Q1 2023.
This dataset contains data for American options, which can be exercised on or before expiration date. This is unlike European options contracts, which can only be exercised on the expiration date.
I am also continuously working on the associated notebook to give a basic idea of how to load and explore the data. Stay tuned!
Similar Datasets: - $TSLA Option Chains - $SPY Option Chains - $NVDA Option Chains - $QQQ Option Chains
Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.
Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.
API Features:
Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.
Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.
Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.
Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.
Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.
Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M veri...
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
ReF Decompile: Relabeling and Function Call Enhanced Decompile
Dataset for ReF Decompile: Relabeling and Function Call Enhanced Decompile
Deploy
python merge.py --output-dir ReF-Decompile vllm serve ReF-Decompile --port 8000 --enable-auto-tool-choice --tool-call-parser mistral python eval.py --base_url http://127.0.0.1:8000/v1
Results
Model/Metrics
Re-executability Rate (%)
Readability (#)
O0O1O2O3AVG… See the full description on the dataset page: https://huggingface.co/datasets/ylfeng/ReF-Decompile-dataset.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
IF YOU FIND THIS CONTENT USEFUL, PLEASE LEAVE AN UPVOTE, COMMENT, AND/OR FOLLOW!
This dataset is a combination of three years of SPDR S&P 500 ETF Trust ($SPY) options end of day quotes ranging from 01-2020 to 12-2022. Each row represents the information associated with one contract's strike price and a given expiration date.
Dates quotes are given in in Unix and in "YYYY-MM-DD HH:MM" formats. Quote frequency is daily at 4:00 pm EST, which corresponds with end of day market closure.
What is an option chain?
An option chain can be defined as the listing of all option contracts. It comes with two different sections: call and put. A call option means a contract that gives you the right but does not give you the obligation to buy an underlying asset at a particular price and within the option's expiration date. This means that in this dataset, there will be the entire option chain (all available option contracts for all expirations) for each business day between Q1 2020 and Q4 2022.
This dataset contains data for American options, which can be exercised on or before expiration data. This is unlike European options contracts, which can only be exercised on the expiration date.
I am also continuously working on the associated notebook to give a basic idea of how to load and explore the data. Stay tuned!
Similar Datasets: - $TSLA Option Chains - $AAPL Option Chains - $NVDA Option Chains - $QQQ Option Chains
Success.ai’s Healthcare Industry Leads Data and B2B Contact Data for US Healthcare Professionals offers an extensive and verified database tailored to connect businesses with key executives and administrators in the healthcare industry across the United States. With over 170M verified profiles, including work emails and direct phone numbers, this dataset enables precise targeting of decision-makers in hospitals, clinics, and healthcare organizations.
Backed by AI-driven validation technology for unmatched accuracy and reliability, this contact data empowers your marketing, sales, and recruitment strategies. Designed for industry professionals, our continuously updated profiles provide the actionable insights you need to grow your business in the competitive healthcare sector.
Key Features of Success.ai’s US Healthcare Contact Data:
Hospital Executives: CEOs, CFOs, and COOs managing top-tier facilities. Healthcare Administrators: Decision-makers driving operational excellence. Medical Professionals: Physicians, specialists, and nurse practitioners. Clinic Managers: Leaders in small and mid-sized healthcare organizations.
AI-Validated Accuracy and Updates
99% Verified Accuracy: Our advanced AI technology ensures data reliability for optimal engagement. Real-Time Updates: Profiles are continuously refreshed to maintain relevance and accuracy. Minimized Bounce Rates: Save time and resources by reaching verified contacts.
Customizable Delivery Options Choose how you access the data to match your business requirements:
API Integration: Connect our data directly to your CRM or sales platform. Flat File Delivery: Receive customized datasets in formats suited to your needs.
Why Choose Success.ai for Healthcare Data?
Best Price Guarantee We ensure competitive pricing for our verified contact data, offering the most comprehensive and cost-effective solution in the market.
Compliance-Driven and Ethical Data Our data collection adheres to strict global standards, including HIPAA, GDPR, and CCPA compliance, ensuring secure and ethical usage.
Strategic Benefits for Your Business Success.ai’s US healthcare professional data unlocks numerous business opportunities:
Targeted Marketing: Develop tailored campaigns aimed at healthcare executives and decision-makers. Efficient Sales Outreach: Engage with key contacts to accelerate your sales process. Recruitment Optimization: Access verified profiles to identify and recruit top talent in the healthcare industry. Market Intelligence: Use detailed firmographic and demographic insights to guide strategic decisions. Partnership Development: Build valuable relationships within the healthcare ecosystem.
Key APIs for Advanced Functionality
Enrichment API Enhance your existing contact data with real-time updates, ensuring accuracy and relevance for your outreach initiatives.
Lead Generation API Drive high-quality lead generation efforts by utilizing verified contact information, including work emails and direct phone numbers, for up to 860,000 API calls per day.
Use Cases
Healthcare Marketing Campaigns Target verified executives and administrators to deliver personalized and impactful marketing campaigns.
Sales Enablement Connect with key decision-makers in healthcare organizations, ensuring higher conversion rates and shorter sales cycles.
Talent Acquisition Source and engage healthcare professionals and administrators with accurate, up-to-date contact information.
Strategic Partnerships Foster collaborations with healthcare institutions and professionals to expand your business network.
Industry Analysis Leverage enriched contact data to gain insights into the US healthcare market, helping you refine your strategies.
Verified Accuracy: AI-driven technology ensures 99% reliability for all contact details. Comprehensive Reach: Covering healthcare professionals from large hospital systems to smaller clinics nationwide. Flexible Access: Customizable data delivery methods tailored to your business needs. Ethical Standards: Fully compliant with healthcare and data protection regulations.
Success.ai’s B2B Contact Data for US Healthcare Professionals is the ultimate solution for connecting with industry leaders, driving impactful marketing campaigns, and optimizing your recruitment strategies. Our commitment to quality, accuracy, and affordability ensures you achieve exceptional results while adhering to ethical and legal standards.
No one beats us on price. Period.
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The exploitation of textual unstructured content (news, company filings, earnings calls etc) in financial analysis is quickly expanding across both quantitative and discretionary strategies as demonstrated by the growing number of academic papers and products in this domain.
The Brain Language Metrics on Earnings Calls Transcripts (BLMECT) dataset has the objective of monitoring several language metrics the quarterly earnings call transcripts for 4500+ US stocks.
The dataset is made of two parts; one includes the language metrics for the most recent earnings call transcript for each stock, namely:
1) Financial sentiment
2) Percentage of words belonging to financial domain classified by language types: - “Constraining” language - “Litigious” language - “Uncertainty” language
3) Readability score
4) Lexical metrics such as lexical density and richness
5) Text statistics such as the report length and the average sentence length
The second part includes the differences between the most recent earnings call transcript and the previous one:
1) Difference of the various language metrics (e.g. delta sentiment, delta readability score delta, delta percentage of a specific language type etc.)
2) Similarity metrics between documents, also with respect to a specific language type (for example similarity with respect to “litigious” language or “uncertainty” language)
The metrics calculation is reported separately for the following sections of the transcript:
a) Management Discussion b) Analysts Questions c) Management Answers to Analysts Questions
The dataset is updated with a daily frequency since new earnings calls transcripts are published every day for some of the universe stocks. Clearly the data for each stock will change on a quarterly basis when new earnings calls are published. The historical dataset is available from year 2012.
Factsheet https://braincompany.co/assets/files/BLM_ECT_summary.pdf
Data dictionary https://braincompany.co/assets/files/BLM_ECT_data_dictionary.pdf