1. Environmental volunteering can benefit participants and nature through improving physical and mental wellbeing while encouraging environmental stewardship. To enhance achievement of these outcomes, conservation organisations need to reach different groups of people to increase participation in environmental volunteering. This paper explores what engages communities searching online for environmental volunteering.
   

2. We conducted a literature review of 1032 papers to determine key factors fostering participation by existing volunteers in environmental projects. We found the most important factor was to tailor projects to the motivations of participants. Also important were: promoting projects to people with relevant interests; meeting the perceived benefits of volunteers and removing barriers to participation.
   

3. We then assessed the composition and factors fostering participation of the NatureVolunteers’s online community (n = 2216) of potential environmental volunteers and compared findings with those from the literature review. We asked whether projects advertised by conservation organisations meet motivations and interests of this online community.
   

4. Using Facebook insights and Google Analytics we found that the online community were on average younger than extant communities observed in studies of environmental volunteering. Their motivations were also different as they were more interested in physical activity and using skills and less in social factors. They also exhibited preference for projects which are outdoor based, and which offer close contact with wildlife. Finally, we found that the online community showed a stronger preference for habitat improvement projects over those involving species-survey based citizen science.
   

5. Our results demonstrate mis-matches between what our online community are looking for and what is advertised by conservation organisations. The online community are looking for projects which are more solitary, more physically active and more accessible by organised transport. We discuss how our results may be used by conservation organisations to better engage with more people searching for environmental volunteering opportunities online.
   

6. We conclude that there is a pool of young people attracted to environmental volunteering projects whose interests are different to those of current volunteers. If conservation organisations can develop projects that meet these interests, they can engage larger and more diverse communities in nature volunteering.
   

Methods The data set consists of separate sheets for each set of results presented in the paper. Each sheet contains the full data, summary descriptive statistics analysis and graphs presented in the paper. The method for collection and processing of the dataset in each sheet is as follows:

The data set for results presented in Figure 1 in the paper - Sheet: "Literature"

We conducted a review of literature on improving participation within nature conservation projects. This enabled us to determine what the most important factors were for participating in environmental projects, the composition of the populations sampled and the methods by which data were collected. The search terms used were (Environment* OR nature OR conservation) AND (Volunteer* OR “citizen science”) AND (Recruit* OR participat* OR retain* OR interest*). We reviewed all articles identified in the Web of Science database and the first 50 articles sorted for relevance in Google Scholar on the 22nd October 2019. Articles were first reviewed by title, secondly by abstract and thirdly by full text. They were retained or excluded according to criteria agreed by the authors of this paper. These criteria were as follows - that the paper topic was volunteering in the environment, including citizen science, community-based projects and conservation abroad, and included the study of factors which could improve participation in projects. Papers were excluded for topics irrelevant to this study, the most frequent being the outcomes of volunteering for participants (such as behavioural change and knowledge gain), improving citizen science data and the usefulness of citizen science data. The remaining final set of selected papers was then read to extract information on the factors influencing participation, the population sampled and the data collection methods. In total 1032 papers were reviewed of which 31 comprised the final selected set read in full. Four factors were identified in these papers which improve volunteer recruitment and retention. These were: tailoring projects to the motivations of participants, promoting projects to people with relevant hobbies and interests, meeting the perceived benefits of volunteers and removing barriers to participation.

The data set for results presented in Figure 2 and Figure 3 in the paper - Sheet "Demographics"

To determine if the motivations and interests expressed by volunteers in literature were representative of wider society, NatureVolunteers was exhibited at three UK public engagement events during May and June 2019; Hullabaloo Festival (Isle of Wight), The Great Wildlife Exploration (Bournemouth) and Festival of Nature (Bristol). This allowed us to engage with people who may not have ordinarily considered volunteering and encourage people to use the website. A combination of surveys and semi-structured interviews were used to collect information from the public regarding demographics and volunteering. In line with our ethics approval, no personal data were collected that could identify individuals and all participants gave informed consent for their anonymous information to be used for research purposes. The semi-structured interviews consisted of conducting the survey in a conversation with the respondent, rather than the respondent filling in the questionnaire privately and responses were recorded immediately by the interviewer. Hullabaloo Festival was a free discovery and exploration event where NatureVolunteers had a small display and surveys available. The Great Wildlife Exploration was a Bioblitz designed to highlight the importance of urban greenspaces where we had a stall with wildlife crafts promoting NatureVolunteers. The Festival of Nature was the UK’s largest nature-based festival in 2019 where we again had wildlife crafts available promoting NatureVolunteers. The surveys conducted at these events sampled a population of people who already expressed an interest in nature and the environment by attending the events and visiting the NatureVolunteers stand. In total 100 completed surveys were received from the events NatureVolunteers exhibited at; 21 from Hullabaloo Festival, 25 from the Great Wildlife Exploration and 54 from the Festival of Nature. At Hullabaloo Festival information on gender was not recorded for all responses and was consequently entered as “unrecorded”.

OVERALL DESCRIPTION OF METHOD DATA COLLECTION FOR ALL OTHER RESULTS (Figures 4-7 and Tables 1-2)

The remaining data were all collected from the NatureVolunteers website. The NatureVolunteers website https://www.naturevolunteers.uk/ was set up in 2018 with funding support from the Higher Education Innovation Fund to expand the range of people accessing nature volunteering opportunities in the UK. It is designed to particularly appeal to people who are new to nature volunteering including young adults wishing to expand their horizons, families looking for ways connect with nature to enhance well-being and older people wishing to share their time and life experiences to help nature. In addition, it was designed to be helpful to professionals working in the countryside & wildlife conservation sectors who wish to enhance their skills through volunteering. As part of the website’s development we created and used an online project database, www.naturevolunteers.uk (hereafter referred to as NatureVolunteers), to assess the needs and interests of our online community. Our research work was granted ethical approval by the Bournemouth University Ethics Committee. The website collects entirely anonymous data on our online community of website users that enables us to evaluate what sort of projects and project attributes most appeal to our online community. Visitors using the website to find projects are informed as part of the guidance on using the search function that this fully anonymous information is collected by the website to enhance and share research understanding of how conservation organisations can tailor their future projects to better match the interests of potential volunteers. Our online community was built up over the 2018-2019 through open advertising of the website nationally through the social media channels of our partner conservation organisations, through a range of public engagement in science events and nature-based festivals across southern England and through our extended network of friends and families, their own social media networks and the NatureVolunteers website’s own social network on Facebook and Twitter. There were 2216 searches for projects on NatureVolunteers from January 1st to October 25th, 2019.

The data set for results presented in Figure 2 and Figure 3 in the paper - Sheet "Demographics"

On the website, users searching for projects were firstly asked to specify their expectations of projects. These expectations encompass the benefits of volunteering by asking whether the project includes social interaction, whether particular skills are required or can be developed, and whether physical activity is involved. The barriers to participation are incorporated by asking whether the project is suitable for families, and whether organised transport is provided. Users were asked to rate the importance of the five project expectations on a Likert scale of 1 to 5 (Not at all = 1, Not really = 2, Neutral = 3, It

This survey aimed to help us gain an understanding of research data stewardship activities in UCC, the scope of those activities, identify any gaps in current resources and skills and work out where the Research Data Service fits with related roles and services. We hoped this activity would also help with the development of a data stewardship network across UCC for support, skills sharing, peer learning and the development of tailored skills development programs within UCC. It would also provide an evidence base to inform the model UCC should adopt in meeting its future research data requirements.

Funders and publishers increasingly require researchers to formally manage their data and encourage or mandate FAIR and/or Open Data outputs. Both the National and European Codes of Research Conduct recognise that data management is central to research integrity and the quality and trustworthiness of research outputs across all disciplines. Research infrastructures in Europe are currently in a phase of development with continued expansion of the European Open Science Cloud (EOSC) and related
services. Successive reports internationally (Realising the EOSC, 2016, Turning FAIR into a Reality, 2018) and our own recently compiled National Landscape Report (NORF, 2021) highlighted a resource and skills gap in meeting the expectations and potential of FAIR research data and related research
infrastructures. Specifically, in relation to FAIR and Open Data, a set of skills, competencies, and responsibilities have been identified and grouped together under the umbrella of a new “Research Data Steward” role. Research data stewardship encompasses all the various tasks and responsibilities that
relate to research data management throughout the entire research lifecycle. The role of data steward is not universally defined yet and is influenced by the context and the needs of the researcher or unit. Across Europe, Research Performing Organisations have taken concrete steps to address this gap, for example by appointing new data steward positions or by re-focusing existing institutional skills and supports into designated competency centres for research data supports. TU Delft is an exemplar where eight newly established embedded data stewards, with domain expertise in the relevant faculty, complement a similar number of support staff based in central services such as the Library and IT Services.

In UCC the Research Data Service provides a range of data stewardship supports to the research community from advisory to tailored training. The Research Data Service and Research Data Coordinator work closely with related services and roles to provide holistic advice on research data management to the UCC research community. The Clinical Research Facility–Cork has also developed a data stewardship service which is available on a consultancy basis to funded human focused research projects. However, the ask of researchers in terms of funder mandated data management plans and commitments to FAIR and Open Data continues to increase. Certainly in the case of the Research Data Service full capacity is fast approaching. As funders embed Open Science, and by extension data management, FAIR, and Open Data more firmly in their policies and requirements there is a risk that this will impact the competitiveness of our funding applications and the reach, impact and quality of our research outputs if we cannot meet researchers increasingly complex needs for research data stewardship support.

We know that there are those engaged in research data stewardship activities throughout UCC although this may not be reflected in their job title. Those who engage in research data stewardship activities do not always identify as Data Stewards but contribute significantly to the data management lifecycle associated with research projects. Each stage of a research project can have specialist data stewardship requirements - these tasks are performed by people in a range of roles and positions including researchers, project managers, data managers, statisticians and data analysts, research assistants, technicians, systems administrators, or research software engineers to name but a few. To develop a holistic and coordinated approach data stewardship and research data management we needed to hear from the whole research ecosystem, those engaged in research and those facilitating it.

Background: Multiple factors, including experiences with unethical research practices, have made some Indigenous groups in the United States and Canada reticent to participate in potentially beneficial health-related research. Yet, Indigenous peoples have also expressed a willingness to participate in research when certain conditions related to the components of data management—including data collection, analysis, security and storage, sharing, dissemination, and withdrawal—are met. A scoping review was conducted to better understand the terms of data management employed in health-related research involving Indigenous communities in the United States and Canada.Methods: PubMed, Embase, PsychINFO, and Web of Science were searched using terms related to the populations and topics of interest. Results were screened and articles deemed eligible for inclusion were extracted for content on data management, community engagement, and community-level research governance.Results: The search strategy returned 734 articles. 31 total articles were extracted, of which nine contained in-depth information on data management and underwent detailed extraction. All nine articles reported the development and implementation of data management tools, including research ethics codes, data-sharing agreements, and biobank access policies.These articles reported that communities were involved in activities and decisions related to data collection (n=7), data analysis (n=5), data-sharing (n=9), dissemination (n=7), withdrawal (n=4), and development of data management tools (n=9). The articles also reported that communities had full or shared ownership of (n=5), control over (n=9), access to (n=1), and possession of data (n=5).All nine articles discussed the role of community engagement in research and community-level research governance as means for aligning the terms of data management with the values, needs, and interests of communities.Conclusions: There is need for more research and improved reporting on data management in health-related research involving Indigenous peoples in the United States and Canada. Findings from this review can provide guidance for the identification of data management terms and practices that may be acceptable to Indigenous communities considering participation in health-related research.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports using the Hi-Tech classification system. The Hi-Tech endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date exports using the End-use classification system. The End-use endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Generated CoT data based on "metaeval/ethics" data(https://huggingface.co/datasets/metaeval/ethics). This is used to fine tine LLMs for the continuation of JPmorgan LLMs research project, which was one of capstone projected offered to students of MSDS program at Columbia University. Because deontology data on hugging face is missing scenario column, the data was generated from raw csv data files in the author's git repo(https://github.com/hendrycks/ethics)

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US Compliance Training For Financial Institutions Market Size 2025-2029

The compliance training for financial institutions market in US size is forecast to increase by USD 1.6 billion billion at a CAGR of 14.7% between 2024 and 2029.

The Compliance Training Market for Financial Institutions in the US is experiencing significant growth, driven by the increasing need for skilled employees to adhere to complex regulatory requirements. This trend is further fueled by the popularity of learning analytics, which enable institutions to measure the effectiveness of their training programs and optimize resources accordingly.


Additionally, the availability of open-source training platforms is democratizing access to compliance education, making it more affordable and accessible for a wider range of financial institutions.These factors, combined with the ever-evolving regulatory landscape, present both opportunities and challenges for market participants. 

What will be the Size of the market During the Forecast Period?

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In the dynamic US financial institutions market, adherence to compliance regulations is paramount. Compliance training modules play a crucial role in ensuring industry standards are met, as institutions face steep penalties for non-compliance. Effectiveness and evaluation of compliance training are essential, with technology enabling innovative delivery methods and platforms. Compliance trends include outsourcing training services and adopting best practices. Risk management and culture are integral components of a robust compliance framework.
Regulatory changes and requirements necessitate continuous training design and consulting. Compliance training programs must address challenges and provide a positive return on investment, utilizing engaging content and effective delivery methods. Compliance innovation and ongoing evaluation are key to staying ahead in this ever-evolving landscape.

How is this market segmented and which is the largest segment?

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

Courses

  Professional courses
  Introductory courses


Delivery Mode

  Offline learning
  Online learning


Training Type

  Anti-Money Laundering (AML)
  Know Your Customer (KYC)
  Data Privacy
  Ethics and Conduct


End-User

  Banks
  Insurance Companies
  Investment Firms
  Credit Unions


Content Type

  Regulatory Compliance
  Risk Management
  Cybersecurity Awareness


Geography

  North America

    US

By Courses Insights

The professional courses segment is estimated to witness significant growth during the forecast period.

The Compliance Training for Financial Institutions market in the US is witnessing notable growth, driven by the increasing demand for professional development in financial compliance. Virtual classroom training and compliance automation solutions are becoming increasingly popular, enabling financial institutions to streamline their compliance program management. Cloud computing technology is also playing a pivotal role in this market, facilitating the delivery of online training and compliance reporting. Anti-money laundering regulations and fraud prevention measures continue to be major focus areas for financial services, leading to a heightened need for compliance training. Big data and machine learning are being leveraged to enhance compliance procedures, while regulatory compliance monitoring and assessments are essential for risk management.

Compliance professionals are in high demand, with credit unions and asset management firms seeking expertise in areas such as know your customer, financial crime, and data privacy. Compliance certifications are a key aspect of professional development, with advanced courses providing detailed descriptions and applications of financial compliance concepts using tools and techniques through exercises. Companies are offering these courses online and on-demand, making them more accessible to professionals. Compliance solutions are being integrated with artificial intelligence and regulatory compliance software to streamline processes and improve efficiency. Overall, the market is undergoing a digital transformation, with a strong emphasis on immersive and harmonious learning experiences that prioritize striking a balance between comprehensive understanding and practical application.

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Market Dynamics

Our US Compliance Training For Financial Institutions Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing str

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports broken down by agricultural and nonagricultural commodities. The USDA endpoint in the Census data API provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports by port and Harmonized System (HS) code. The Port HS endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the port level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date exports by state and Harmonized System (HS) code. The State HS endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the state level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Terms of Use

Please read the terms of use below.

The National Institute of Justice (NIJ) adheres to codes of conduct that are generally accepted in higher education and scientific research for the exchange and proper use of knowledge and information.

These data are distributed under the following terms of use. By continuing past this point to the data retrieval process, you signify your agreement to comply with the requirements stated below:

Privacy and Confidentiality of Data

Any intentional identification of a individuals or unauthorized disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, users of data agree:

• To use these datasets solely for research or statistical purposes and not for investigation of specific individuals • To make no use of the identity of any individual discovered inadvertently, and to advise NIJ of any such discovery (NIJRecidivismChallenge@usdoj.gov)

Federal law and regulations require that research data collected by the U.S. Department of Justice or by its grantees and contractors may only be used for research or statistical purposes. The applicable laws and regulations may be found in the United States Code, 34 USC Section 10231(a), the Code of Federal Regulations, 28 CFR 22, and 62 F.R. 35044 (June 27, 1997) (The Federal Confidentiality Order). Accordingly, any intentional identification or disclosure of a person or establishment may violate federal law as well as the assurances of confidentiality given to the providers of the information. Therefore, users of this data must agree to abide by these regulations and understand that NIJ may report any potential violation to the appropriate entities within the U.S. Department of Justice.

Academic conferences are important institutions for promoting new research and facilitating conversations about the field. As a venue for knowledge exchange, professional development, and networking, conferences ideally promote positive environments that make scholars from underrepresented groups feel welcome. Yet negative experiences at conferences are well-documented. Codes of conduct have been promoted as tools to re- duce harassment and discrimination. We examine the prevalence and content of codes at U.S.-based political science conferences and workshops. More specifically, we analyze whether and how codes address issues of sexual misconduct and identity-based discrimination. We find that 19% of 177 surveyed conferences have codes. Conferences that are older and larger are more likely to have codes, as are conferences that are run by organizations with permanent staff and relevant committees. We argue that effective conference codes must contain definitions, reporting channels, and enforcement procedures. Many of the analyzed codes did not explicitly define prohibited behaviors, specify mechanisms to report code violations, or describe consequences for misconduct.

The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables convey 1. demographics (281 variables), 2. dietary consumption (324 variables), 3. physiological functions (1,040 variables), 4. occupation (61 variables), 5. questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood), 6. medications (29 variables), 7. mortality information linked from the National Death Index (15 variables), 8. survey weights (857 variables), 9. environmental exposure biomarker measurements (598 variables), and 10. chemical comments indicating which measurements are below or above the lower limit of detection (505 variables).

csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file. - The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments. - "dictionary_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES. - "dictionary_harmonized_categories.csv" contains the harmonized categories for the categorical variables. - “dictionary_drug_codes.csv” contains the dictionary for descriptors on the drugs codes. - “nhanes_inconsistencies_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.

R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file. - “w - nhanes_1988_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data. - “m - nhanes_1988_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.

Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order. - “example_0 - merge_datasets_together.Rmd” demonstrates how to merge the curated NHANES datasets together. - “example_1 - account_for_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model. - “example_2 - calculate_summary_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design. - “example_3 - run_multiple_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design.

Lovoo v3 Dating App User Profiles and Statistics

Revealing popular user traits and behavior

By Jeffrey Mvutu Mabilama [source]

About this dataset

When Dating apps like Tinder began to become more popular, users wanted to create the best profiles possible in order to maximize their chances of being noticed and gain more potential encounters. Unlike traditional dating platforms, these new ones required mutual attraction before allowing two people to chat, making it all the more important for users to create a great profile that would give them an advantage over others.

It was amidst this scene that we Humans began paying attention at how charismatic and inspiring people presented themselves online. The most charismatic individuals tended to be the ones with the most followers or friends on social networks. This made us question what makes a great user profile and how one could make a lasting first impression in order ensure finding true love or even just some new friendships? How do we recognize a truly charismatic person from their presentation on social media? Is there any way of quantifying charisma?

In 2015 I set out with researching all this using Lovoo's newest dating app version -V3 (the iOS version), gathering user profile data such as age demographics, interest types (friendship, chatting or dating), language preferences etc., as well as usually unavailable metrics like number of profile visits, kisses received etc. I was also able to collect pictures of those user profiles in order discern any correlations between appeal and reputation that may have existed at that time amongst Lovoo's population base.

My goal is forthis dataset will help you answer those questions related not just romantic success but also popularity/charisma censes/demographic studies and even detect influential figures both within & outside Lovoo's platform . A starter analysis is available accompanying this dataset which can be used as a reference point when working with the data here. Using this dataset you can your own investigations into:

  * What type of person has attracted more visitors or potential matches than others?   
  * Which criteria can be used when determining someone’s charm/likability among others    ?
  * How does one optimize his/her dating app profile visibility so he/she won’t remain unseen among other users? 

Grab this amazing opportunity now! Kick-start your journey towards understanding the inner workings behind success in online relationships today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started with this dataset first you need to download it from Kaggle. Once downloaded you should take a look at the column names in order to get an idea of what information is available. This data includes fields such as gender, age name (and nickname), number of pictures uploaded/profile visits/kisses /fans/gifts received and flirt interests (chatting or making friends). It also contains language specifics like detected languages for each user as well as country & city of residence.

The most interesting section for your research is likely the number of details that have been filled in for each user – such as whether they are interested in chatting or making friends. Usually these information points allow us to infer more about a person’s character – from jokester to serious individualist (or anything else!). The same holds true for their language preferences which might reveal aspects regarding their cultures orientation or habits.

You may also want collected data which was left out here - imagery associated with users' profiles - so please contact JfreexDatasets_bot on Telegram if you would like access to this imagery that has not yet been uploaded here on Kaggle but is intregral part of understanding what makes a great user profile attractive on these platforms according Aesthetics Theory applied in an uthentic way when considering how each image adds sentimental appeal value by its perspective content focus - be it visually descriptive; emotive narrative; personality coupled with expression mood association.. etcetera... Or simple just download relevant images yourself using automated scripts ready made via webiste Grammak where Github Repo exists: https://github.com/grammak580542008/Lovoo-v3-Profiles-Data # 1 year ago...

Finally moving ahead — keep in mind that there are other ways data can be gathered possible besides just downloading it from Kaggle – such us Messenger Bots or Customer Relationship Management systems which help companies serve...

Objective: To assess US physicians' attitudes towards using shared decision-making (SDM) to achieve cost containment. Design: Cross-sectional mailed survey. Setting: US medical practice. Participants: 3897 physicians were randomly selected from the AMA Physician Masterfile. Of these, 2556 completed the survey. Main outcome measures: Level of enthusiasm for "Promoting better conversations with patients as a means of lowering healthcare costs"; degree of agreement with "Decision support tools that show costs would be helpful in my practice" and agreement with "should promoting SDM be legislated to control overall healthcare costs". Results: Of 2556 respondents (response rate (RR) 65%), two-thirds (67%) were 'very enthusiastic' about promoting SDM as a means of reducing healthcare costs. Most (70%) agreed decision support tools that show costs would be helpful in their practice, but only 24% agreed with legislating SDM to control costs. Compared with physicians with billing-only compensation, respondents with salary compensation were more likely to strongly agree that decision support tools showing costs would be helpful (OR 1.4; 95% CI 1.1 to 1.7). Primary care physicians (vs surgeons, OR 1.4; 95% CI 1.0 to 1.6) expressed more enthusiasm for SDM being legislated as a means to address healthcare costs. Conclusions: Most US physicians express enthusiasm about using SDM to help contain costs. They believe decision support tools that show costs would be useful. Few agree that SDM should be legislated as a means to control healthcare costs.

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CodeM: Can Programming Languages Boost Each Other via Instruction Tuning?

Paper GitHub

  Abstract

When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript… See the full description on the dataset page: https://huggingface.co/datasets/Daoguang/CodeM-Multilinugal-Data.

According to data gathered in 2023, over 90 percent of surveyed authors in the United States felt that readers should know when AI created all or portions of a work, and that the publishing industry should adopt a code of conduct or ethical approach to AI. Concerns were also raised that publishers may use AI to generate books in their entirety.