The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by over 1,400 OSDG Community Platform (OSDG-CP) citizen scientists from over 140 countries, with respect to the Sustainable Development Goals (SDGs).

Dataset Information

In support of the global effort to achieve the Sustainable Development Goals (SDGs), OSDG is realising a series of SDG-labelled text datasets. The OSDG Community Dataset (OSDG-CD) is the direct result of the work of more than 1,400 volunteers from over 130 countries who have contributed to our understanding of SDGs via the OSDG Community Platform (OSDG-CP). The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches.

📘 The file contains 43,0210 (+390) text excerpts and a total of 310,328 (+3,733) assigned labels.

To learn more about the project, please visit the OSDG website and the official GitHub page. Explore a detailed overview of the OSDG methodology in our recent paper "OSDG 2.0: a multilingual tool for classifying text data by UN Sustainable Development Goals (SDGs)".

Source Data

The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as SDG-Pathfinder and SDG Library. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average.

Methodology

All the texts are evaluated by volunteers on the OSDG-CP. The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge.

In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label.

There are 3 types of exercises:

All volunteers start with the mandatory introductory exercise that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform.

SDG-specific exercises where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty.

All SDGs exercise where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label.

After finishing the introductory exercise, the volunteer is free to select either SDG-specific or All SDGs exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still.

To ensure quality, each text is validated by up to 9 different volunteers and all texts included in the public release of the data have been validated by at least 3 different volunteers.

It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either accept or reject a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label.

Column Description

doi - Digital Object Identifier of the original document

text_id - unique text identifier

text - text excerpt from the document

sdg - the SDG the text is validated against

labels_negative - the number of volunteers who rejected the suggested SDG label

labels_positive - the number of volunteers who accepted the suggested SDG label

agreement - agreement score based on the formula (agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}})

Further Information

Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai.

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

STATA

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 45 institutions beginning in March 2016 and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 6,200 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Singh, Manmeet. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2023. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-07-07. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

Volunteering is seen as an effective way to address a wide range of issues that are present in modern societies, in particular in rural areas. In order to gain insight into the current state of volunteering, a survey was carried out with the aim of providing an inventory of the digitalisation of volunteering and addressing the influence of spatiality in this context. This article examines the traditional data collection methods of surveys that have been used in the past to collect information on volunteering and presents a different sampling procedure based on a random sample of municipalities, due to insufficient data available from official authorities. Finally, the article discusses the results of the survey and analyses how the unique sampling procedure differs from previous approaches. The comparison with a larger survey shows that the different sampling methods produce largely identical results, at least for the central variable of organisational area. As long as no structured data are available from the public authorities, there will always be a trade-off with regard to sampling strategies and the method of contact when doing research on volunteering organisations.

This dataset contains citizen science projects in the Netherlands. Citizen science is scientific work that citizens completely or partially conduct themselves on a voluntary basis. Professional researchers often provide instructions and guidance, but they are not the main collectors of the data. Alongside project names, the dataset includes supplementary information regarding a description of the project, its organizers, location, duration, data accessibility, and links to the main project information and data if applicable.

The CSV and XLSX files provide an up-to-date list of both closed and ongoing citizen science projects in the Netherlands categorized by discipline. Anyone can add or update a citizen science project, alongside its supplementary information, using the main GitHub repository (https://github.com/sodascience/awesome-citizen-science-nl). It is only possible to add projects conducted in the Netherlands and we encourage other countries to create such a list themselves. We provide further descriptions of the supplementary information and their input format in the codebook, which can be found on Github and in this repository. We update the dataset as additional projects are added and approved.

This project received support of the SoDa team of ODISSEI (https://odissei-data.nl/en/using-soda/) to make the dataset openly available via Github and publish the data on Zenodo.

This dataset relates to the ESRC Citizen Contribution to Local Public Services project. It is the first of four datasets created for this project. The project sought to identify how social information could influence volunteering levels in different groups, using four different field experiments. This dataset contains information from the first field experiment which examined one form of social information - i.e. endorsement - on a large student population. The other datasets in the project (2,3,4) examine other forms of social information with different groups of people.

This dataset contains information on forms of, and time contributed to, volunteering over a 7 week period for over 100,000 students from 5 UK universities randomly allocated to receive different forms of email endorsement about volunteering, or to a control email. The email endorsements were from politicians, celebrities or student peers. The dataset includes demographic data provided by student records services, and information on volunteering outcomes provided by universities and web-checking software, i.e. whether students clicked through from the email they received to find out more about volunteering, whether they registered as a volunteer, took up a volunteering placement or attended volunteer training. The dataset also includes self-reported survey data on the nature and extent of volunteering over the same 7 week time period.

Citizen contributions to public services are regarded as increasingly important by researchers and policy-makers. These include volunteering to make communities better places. A core idea in recent thinking in behavioural economics and the study of collective action is that the way information is presented to citizens matters to their willingness to donate their time. This can include who makes the request, what information there is about what other people do, and what feedback people get about their volunteering and the activities of others.

The research is particularly interested in whether recommendations from prominent people, such as those in the community sector and politicians, can help promote civic action. The research also examines the effect of providing feedback about other people's time contributions. The researchers want to know whether varying the form of feedback to citizens matters, and use randomized controlled trials to investigate their effects on contributions to volunteering.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

The COVID Tracking Project was a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States. Our dataset was in use by national and local news organizations across the United States and by research projects and agencies worldwide.

Every day, we collected data on COVID-19 testing and patient outcomes from all 50 states, 5 territories, and the District of Columbia by visiting official public health websites for those jurisdictions and entering reported values in a spreadsheet. The files in this dataset represent the entirety of our COVID-19 testing and outcomes data collection from March 7, 2020 to March 7, 2021. This dataset includes official values reported by each state on each day of antigen, antibody, and PCR test result totals; the total number of probable and confirmed cases of COVID-19; the number of people currently hospitalized, in intensive care, and on a ventilator; the total number of confirmed and probable COVID-19 deaths; and more.

Methods This dataset was compiled by about 300 volunteers with The COVID Tracking Project from official sources of state-level COVID-19 data such as websites and press conferences. Every day, a team of about a dozen available volunteers visited these official sources and recorded the publicly reported values in a shared Google Sheet, which was used as a data source to publish the full dataset each day between about 5:30pm and 7pm Eastern time. All our data came from state and territory public health authorities or official statements from state officials. We did not automatically scrape data or attempt to offer a live feed. Our data was gathered and double-checked by humans, and we emphasized accuracy and context over speed. Some data was corrected or backfilled from structured data provided by public health authorities. Additional information about our methods can be found in a series of posts at http://covidtracking.com/analysis-updates.

We offer thanks and heartfelt gratitude for the labor and sacrifice of our volunteers. Volunteers on the Data Entry, Data Quality, and Data Infrastructure teams who granted us permission to use their name publicly are listed in VOLUNTEERS.md.

Abstract copyright UK Data Service and data collection copyright owner.

The Community Life Survey (CLS) is a household survey conducted in England, tracking the latest trends and developments across areas key to encouraging social action and empowering communities, including: volunteering and charitable giving; views about the local area; community cohesion and belonging; community empowerment and participation; influencing local decisions and affairs; and subjective well-being and loneliness.

The CLS was first commissioned by the Cabinet Office in 2012. From 2016-17, the Department for Digital, Culture, Media and Sport (DCMS) took over responsibility for publishing results. During 2020, the DCMS also commissioned the Community Life COVID-19 Re-contact Survey (CLRS) (SN 8781) to provide data on how the COVID-19 pandemic has affected volunteering, charitable giving, social cohesion, wellbeing and loneliness in England.

Background
Up to 2015-16, the survey used a face-to-face methodology. Following thorough testing (experimental online versions of the survey were released for 2013-14, 2014-15 and 2015-16), the CLS moved online from 2016-17 onwards, with an end to the previous face-to-face method. The survey uses a push-to-web methodology (with paper mode for those who are not digitally engaged). The survey informs and directs policy and action in these areas;

• to provide data of value to all users, including public bodies, external stakeholders and the public; and
• underpin further research and debate on building stronger communities.

The Community Life Survey incorporates a small number of priority measures from the Citizenship Survey, which ran from 2001-2011, conducted by the then Department for Communities and Local Government. These measures were incorporated in the Community Life Survey so that trends in these issues could continue to be tracked over time. (The full Citizenship Survey series is held at the UK Data Archive under GNs 33347 and 33474.)

Further information may be found on the GOV.UK Community Life Survey webpage.

The Community Life Survey Experimental Online Data, 2013-2014 includes the data from a project testing the viability of an online alternative to the face-to-face survey. This dataset covers the 2013-2014 online survey, with a sample size of 10,215 adults (aged 16 years and over) in England, which ran from June 2013 to March 2014. Data from a postal version of the questionnaire, which was available on request, is also included in the dataset. This questionnaire covered the same topics as the online survey but was reduced in length. Full details can be found in the Web Survey Technical Report which is available in the Documentation section below.

End User Licence and Special Licence data
Users should note that there are two versions of each Community Life Survey Experimental Online Data experimental online dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains more detailed variables relating to: social class; ethnicity; religion; sexual identity and lower level geographical classifications.

The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. Therefore, users are strongly advised to order the standard version of the data.

The SL version of the Community Life Survey Experimental Online Data, 2013-2014 is held under SN 7738.


Main Topics:

The main topics include: identity and social networks, local community, civic engagement, volunteering, social action, subjective well-being and basic demographics.

First emerging at the end of the 20th century, community science (a.k.a. citizen science), utilizing volunteers and volunteer-contributed data, has become a major component of biological conservation worldwide. Interacting factors including limited funding, technology availability, and public interest present a unique opportunity for conservation scientists and practitioners to benefit from larger datasets with broader spatial and temporal reach than possible from professional researchers alone. In tandem with benefits to science, volunteers gain hands-on research experience, scientific knowledge, outdoor opportunities, and community. However, this approach is far from universally adopted, as some researchers question the accuracy and value of community science data and entrusting volunteers with sensitive data, such as rare plant locations. We assessed community science projects that conduct rare plant monitoring to examine the value of community science in plant conservation. We identified projects through research and targeted outreach. Through digital surveys of project managers and volunteers, we collected qualitative and quantitative data addressing the efficacy of projects in regard to a number of predictor variables (e.g., staffing, funding, program size, data management, volunteer training, and demographics) and metrics of success (e.g., number of volunteers engaged, monitoring assignments, and publications). We reported the qualities of successful plant conservation community science projects to encourage the establishment of new projects, the improvement of existing ones, and the maximum application of volunteer-contributed rare plant monitoring datasets. We propose the establishment of a community science rare plant monitoring network to facilitate sharing ideas, strategies, and tools for project success.

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies.The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA) and the National Water Quality Monitoring Council (NWQMC) that integrates publicly available water quality data from the USGS National Water Information System (NWIS) the EPA STOrage and RETrieval (STORET) Data Warehouse, and the USDA ARS Sustaining The Earth’s Watersheds - Agricultural Research Database System (STEWARDS) .The EPA water quality data originate from the STORET Data Warehouse, which is the EPA's repository of water quality monitoring data collected by water resource management groups across the country. Organizations, including states, tribes, watershed groups, other federal agencies, volunteer groups and universities, submit data to the STORET Warehouse in order to make their data publicly accessible. For more information about STORET, see the STORET Home Page.The USGS water quality data originate from the NWISWeb Database. The database contains current and historical water data from more than 1.5 million sites across the nation and is used by state and local governments, public and private utilities, private citizens, and other federal agencies involved with managing our water resources. The Water Quality Portal serves data from the NWISWeb database. All publicly available data are not available through the web services at this time. The web services include results for USGS parameters represented in the crosswalk between NWIS Parameter Codes and the EPA Substance Registry System. The latest crosswalk is available at this WQP Page. At this time this crosswalk accounts for approximately 92% of water-quality data available from NWISWeb.USGS Biological data come from the BioData Retreval system. The U.S. Geological Survey (USGS) BioData Retrieval system provides access to aquatic bioassessment data (biological community and physical habitat data) collected by USGS scientists from stream ecosystems across the nation. USGS scientists collect fish-, aquatic macroinvertebrate-, and algae-community samples and conduct stream physical habitat surveys as part of its fundamental mission to describe and understand the Earth. The publicly available BioData Retrieval system disseminates data from over 15,000 fish, aquatic macroinvertebrate, and algae community samples. Additionally, the system serves data from over 5000 physical data sets (samples), such as reach habitat and light availability, that were collected to support the community sample analyses. The system contains sample data that were collected and processed since 1991 using the protocols of the National Water-Quality Assessment (NAWQA). As of 2010, the system has added data collected by USGS scientists using the procedures and protocols of the U.S. Environmental Protection Agency National Rivers and Streams Assessment program (NRSA). The ARS water quality data originate from the STEWARDS (Sustaining The Earth’s Watersheds - Agricultural Research Database System) database and were developed as part of the Conservation Effects Assessment Program (CEAP). STEWARDS contains long-term hydrologic, weather, and water quality data from 17 ARS benchmark research watersheds. For more information about STEWARDS, see the STEWARDS Home Page.

In 1990, INDEPENDENT SECTOR commissioned the Gallup Organization to conduct a national survey on the giving and volunteering behavior of Americans. This survey is the second of a series of surveys that are conducted every two years in order to establish reliable trend information. This new series of national surveys include a wealth of new details on giving, volunteering, and motivations for such behavior. The 1990 survey continues to build on the information collected in the 1988 survey in order to establish trend data over a period of time. This survey addresses the following questions: 1. Who gives and volunteers? To whom? How much? 2. What determines giving and volunteering behavior? 3. What are the motivations for giving and volunteering to various types of charitable causes? 4. Is there a relationship between giving and volunteering to religious organizations and giving and volunteering to other charities? 5. What are public attitudes toward giving, volunteering, and the performance of charitable organizations? The study gives special attention to the demographic characteristics of givers and volunteers. Since the demography of the American population is changing rapidly, it is especially important to trace the level of participation of various population groups in the tradition of giving and voluntary service. Therefore, special attention is given to the giving and volunteering behavior of blacks, Hispanics, and recent immigrants. This study continues INDEPENDENT SECTOR's effort to encourage and occasionally sponsor research on motivations for giving and volunteering. In this survey, all participants were asked a series of questions relating to their personal goals, values, and attitudes toward contributing time and money. Participants also were asked about the people and institutions, such as their employer, who influenced or encouraged their voluntary behavior. Furthermore, detailed questions were asked about giving and volunteering to a wide variety of nonprofit organizations, including religious organizations.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31089321. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

This collection houses PDFs of active BBS route maps, which are grouped by province or territory. These maps allow BBS volunteers in Canada to easily locate the start of their route, and to navigate the official route. The BBS is jointly coordinated by Environment Canada's Canadian Wildlife Service and the U.S. Geological Survey (USGS), Patuxent Wildlife Research Center. Any use of BBS data for Canada should acknowledge the hundreds of skilled volunteers in Canada who have participated in the BBS over the years, those who have served as provincial or territorial coordinators for the BBS, and the Boreal Avian Modelling Project (BAM; www.borealbirds.ca), whose collaboration was invaluable to the creation of the BBS route and stop location dataset. General information on the BBS in Canada (e.g., the data collection methods, survey coverage, etc.) can be found here: https://www.canada.ca/en/environment-climate-change/services/bird-surveys/landbird/north-american-breeding/overview.html Current Canadian BBS trend results and annual indices, as well as detailed descriptions of the information provided in these estimates and the statistical methods used, are available through the Canadian BBS results website: https://wildlife-species.canada.ca/breeding-bird-survey-results Raw BBS data are publicly accessible through the Patuxent Wildlife Research Center's data portal: https://www.pwrc.usgs.gov/bbs/RawData/

OpenCon 2015 Application Open Data

The purpose of this document is to accompany the public release of data collected from OpenCon 2015 applications.Download & Technical Information The data can be downloaded in CSV format from GitHub here: https://github.com/RightToResearch/OpenCon-2015-Application-Data The file uses UTF8 encoding, comma as field delimiter, quotation marks as text delimiter, and no byte order mark.

License and Requests

This data is released to the public for free and open use under a CC0 1.0 license. We have a couple of requests for anyone who uses the data. First, we’d love it if you would let us know what you are doing with it, and share back anything you develop with the OpenCon community (#opencon / @open_con ). Second, it would also be great if you would include a link to the OpenCon 2015 website (www.opencon2015.org) wherever the data is used. You are not obligated to do any of this, but we’d appreciate it!

Data Fields

Unique ID

This is a unique ID assigned to each applicant. Numbers were assigned using a random number generator.

Timestamp

This was the timestamp recorded by google forms. Timestamps are in EDT (Eastern U.S. Daylight Time). Note that the application process officially began at 1:00pm EDT June 1 ended at 6:00am EDT on June 23. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. [a]

Gender

Mandatory. Choose one from list or fill-in other. Options provided: Male, Female, Other (fill in).

Country of Nationality

Mandatory. Choose one option from list.

Country of Residence

Mandatory. Choose one option from list.

What is your primary occupation?

Mandatory. Choose one from list or fill-in other. Options provided: Undergraduate student; Masters/professional student; PhD candidate; Faculty/teacher; Researcher (non-faculty); Librarian; Publisher; Professional advocate; Civil servant / government employee; Journalist; Doctor / medical professional; Lawyer; Other (fill in).

Select the option below that best describes your field of study or expertise

Mandatory. Choose one option from list.

What is your primary area of interest within OpenCon’s program areas?

Mandatory. Choose one option from list. Note: for the first approximately 24 hours the options were listed in this order: Open Access, Open Education, Open Data. After that point, we set the form to randomize the order, and noticed an immediate shift in the distribution of responses.

Are you currently engaged in activities to advance Open Access, Open Education, and/or Open Data?

Mandatory. Choose one option from list.

Are you planning to participate in any of the following events this year?

Optional. Choose all that apply from list. Multiple selections separated by semi-colon.

Do you have any of the following skills or interests?

Mandatory. Choose all that apply from list or fill-in other. Multiple selections separated by semi-colon. Options provided: Coding; Website Management / Design; Graphic Design; Video Editing; Community / Grassroots Organizing; Social Media Campaigns; Fundraising; Communications and Media; Blogging; Advocacy and Policy; Event Logistics; Volunteer Management; Research about OpenCon's Issue Areas; Other (fill-in).

Data Collection & Cleaning

This data consists of information collected from people who applied to attend OpenCon 2015. In the application form, questions that would be released as Open Data were marked with a caret (^) and applicants were asked to acknowledge before submitting the form that they understood that their responses to these questions would be released as such. The questions we released were selected to avoid any potentially sensitive personal information, and to minimize the chances that any individual applicant can be positively identified. Applications were formally collected during a 22 day period beginning on June 1, 2015 at 13:00 EDT and ending on June 23 at 06:00 EDT. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. Applications were collected using a Google Form embedded at http://www.opencon2015.org/attend, and the shortened bit.ly link http://bit.ly/AppsAreOpen was promoted through social media. The primary work we did to clean the data focused on identifying and eliminating duplicates. We removed all duplicate applications that had matching e-mail addresses and first and last names. We also identified a handful of other duplicates that used different e-mail addresses but were otherwise identical. In cases where duplicate applications contained any different information, we kept the information from the version with the most recent timestamp. We made a few minor adjustments in the country field for cases where the entry was obviously an error (for example, electing a country listed alphabetically above or below the one indicated elsewhere in the application). We also removed one potentially offensive comment (which did not contain an answer to the question) from the Gender field and replaced it with “Other.”

About OpenCon

OpenCon 2015 is the student and early career academic professional conference on Open Access, Open Education, and Open Data and will be held on November 14-16, 2015 in Brussels, Belgium. It is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. The meeting will convene students and early career academic professionals from around the world and serve as a powerful catalyst for projects led by the next generation to advance OpenCon's three focus areas—Open Access, Open Education, and Open Data. A unique aspect of OpenCon is that attendance at the conference is by application only, and the majority of participants who apply are awarded travel scholarships to attend. This model creates a unique conference environment where the most dedicated and impactful advocates can attend, regardless of where in the world they live or their access to travel funding. The purpose of the application process is to conduct these selections fairly. This year we were overwhelmed by the quantity and quality of applications received, and we hope that by sharing this data, we can better understand the OpenCon community and the state of student and early career participation in the Open Access, Open Education, and Open Data movements.

Questions

For inquires about the OpenCon 2015 Application data, please contact Nicole Allen at nicole@sparc.arl.org.

The Emergency Management Institute (EMI) Independent Study Program (ISP) provides web-based training related to all aspects of emergency management for the whole community. The EMI ISP is a distance learning program that offers training free of charge via the public-facing web site training.fema.gov. It serves as both classroom alternative to delivering valuable training to the professional and volunteer emergency management community and an opportunity to improve public awareness and promote disaster preparedness nationally. The EMI ISP offers more than 195 training courses.

For questions about this data please contact HealthOpenData@minneapolismn.govFor more information about Air Quality in the City of Minneapolis, check the Health Department's page on the City websiteAttribute InformationDate – The month and year during which this sample was collected. Each sample is collected over a period of 72-hours. The exact dates of collection are not specified on this dataset.Can ID – A unique identifier for this sample and is determined by concatenating the collecting Summa canister’s identification number with the month and year of collection.Parameter – The chemical (for this study, all are Volatile Organic Compounds, or VOCs) for which the result is reported.Results – The amount of the particular VOC (parameter) detected in this sample. All results above the detection limit are reported in this dataset.Units – The units in which the result is reported. All results are reported in units of micrograms per cubic meter (µg/m3).CAS – The CAS Registry Number, also referred to as CASRN or CAS Number, is a unique numerical identifier assigned by Chemical Abstracts Service to every chemical substance described in the open scientific literature.HRV – A Health Risk Value (HRV) is the concentration of a chemical that is likely to pose little or no risk to human health and is being used by our study as a reference level for comparing the results. Most HRVs are expressed as concentrations of micrograms of chemical per cubic meter of air (µg/m3). HRVs are developed to protect human health for a specified length of exposure (e.g., one hour, 13 weeks, a lifetime). In cases where an HRV promulgated using the public process described in the Administrative Procedures Act (Minnesota Statutes Chapter 14) was not available as a reference level, we instead used the recommended exposure limit (REL) from the National Institute for Occupational Safety and Health (NIOSH) or the permissible exposure limit (PEL) from the Occupational Safety and Health Administration (OSHA), selecting the most conservative level. For purposes of our study, all reference levels (HRVs, RELs, and PELs) will be referred to as a Health Risk Value.Units - The units associated with the HRV, REL, or PEL reference level. All reference levels have been converted to units of micrograms per cubic meter (µg/m3).HRV Types – The type (HRV, REL, or PEL) of reference level selected. For the purpose of this study, all are referred to as a Health Risk Value. The value selected was the lowest, most conservative value.Name – The name of the location. Volunteer names and business names are left blank.Description – The type of location where the canister was placed. Volunteer locations are typically residential sites where the property owner/occupant hosts a sampling Summa canister. Business locations, which are also VOC emitters, who are voluntarily participating in the study. MPCA locations are sites of existing Minnesota Pollution Control Agency air monitors sampling for VOCs where a sampling Summa canister is placed for data comparison. MPRB locations are Minneapolis Park and Recreation Board properties. Sponsored locations are sites where the community has paid for additional sampling Summa canisters to be placed.Address – The address at or near where the sampling Summa canister was placed.City – For mapping purposes, we have included the city where the sampling Summa canister was placed (all are within the Minneapolis boundary).State – For mapping purposes, we have included the state where the sampling Summa canister was placed.Zip – For mapping purposes, we have included the zip code where the sampling Summa canister was placed.

This is the full set of images submitted for the Eclipse Megamovie project, a citizen science project to capture images of the Sun’s corona during the August 21, 2017 total solar eclipse. These images were taken by volunteer photographers (as well as the general public) from across the country using consumer camera equipment. The Eclipse Megamovie project was a collaboration between UC Berkeley, Google, the Astronomical Society of the Pacific, and many more.* In addition to the dataset, the code used by the project to create the website and process individual movies can be found in GitHub For a full description of the data fields, see below. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . *Additional partners: Center for Research on Lifelong STEM Learning, Oregon State University, Eclipse Across America, Foothill College, High Altitude Observatory of the National Center for Atmospheric Research, Ideum, Lick Observatory, Space Sciences Laboratory, University of California, Berkeley, University of Colorado at Boulder, Williams College and the IAU Working Group.

RESEARCH APPROACH

The research approach adopted for the study consists of seven phases which includes as shown in Figure 1:

1. Pre-acquisition
2. data pre-processing
3. Raw images collection
4. Image pre-processing
5. Naming of images
6. Dataset Repository
7. Performance Evaluation

The different phases in the study are discussed in the sections below.

PRE-ACQUISITION

The volunteers are given brief orientation on how their data will be managed and used for research purposes only. After the volunteers agrees, a consent form is given to be read and signed. The sample of the consent form filled by the volunteers is shown in Figure 1.

The capturing of images was started with the setup of the imaging device. The camera is set up on a tripod stand in stationary position at the height 90 from the floor and distance 20cm from the subject.

EAR IMAGE ACQUISITION

Image acquisition is an action of retrieving image from an external source for further processing. The image acquisition is purely a hardware dependent process by capturing unprocessed images of the volunteers using a professional camera. This was acquired through a subject posing in front of the camera. It is also a process through which digital representation of a scene can be obtained. This representation is known as an image and its elements are called pixels (picture elements). The imaging sensor/camera used in this study is a Canon E0S 60D professional camera which is placed at a distance of 3 feet form the subject and 20m from the ground.

This is the first step in this project to achieve the project’s aim of developing an occlusion and pose sensitive image dataset for black ear recognition. (OPIB ear dataset). To achieve the objectives of this study, a set of black ear images were collected mostly from undergraduate students at a public University in Nigeria.

The image dataset required is captured in two scenarios:

1. uncontrolled environment with a surveillance camera

The image dataset captured is purely black ear with partial occlusion in a constrained and unconstrained environment.

2. controlled environment with professional cameras

The ear images captured were from black subjects in controlled environment. To make the OPIB dataset pose invariant, the volunteers stand on a marked positions on the floor indicating the angles at which the imaging sensor was captured the volunteers’ ear. The capturing of the images in this category requires that the subject stand and rotates in the following angles 60^o, 30^o and 0^o towards their right side to capture the left ear and then towards the left to capture the right ear (Fernando et al., 2017) as shown in Figure 4. Six (6) images were captured per subject at angles 60^o, 30^o and 0^o for the left and right ears of 152 volunteers making a total of 907 images (five volunteers had 5 images instead of 6, hence folders 34, 22, 51, 99 and 102 contain 5 images).

To make the OPIB dataset occlusion and pose sensitive, partial occlusion of the subject’s ears were simulated using rings, hearing aid, scarf, earphone/ear pods, etc. before the images are captured.

CONSENT FORM This form was designed to obtain participant’s consent on the project titled: An Occlusion and Pose Sensitive Image Dataset for Black Ear Recognition (OPIB). The information is purely needed for academic research purposes and the ear images collected will curated anonymously and the identity of the volunteers will not be shared with anyone. The images will be uploaded on online repository to aid research in ear biometrics. The participation is voluntary, and the participant can withdraw from the project any time before the final dataset is curated and warehoused. Kindly sign the form to signify your consent. I consent to my image being recorded in form of still images or video surveillance as part of the OPIB ear images project. Tick as appropriate: GENDER Male Female AGE (18-25) (26-35) (36-50) ……………………………….. SIGNED

Figure 1: Sample of Subject’s Consent Form for the OPIB ear dataset

RAW IMAGE COLLECTION

The ear images were captured using a digital camera which was set to JPEG because if the camera format is set to raw, no processing will be applied, hence the stored file will contain more tonal and colour data. However, if set to JPEG, the image data will be processed, compressed and stored in the appropriate folders.

IMAGE PRE-PROCESSING

The aim of pre-processing is to improve the quality of the images with regards to contrast, brightness and other metrics. It also includes operations such as: cropping, resizing, rescaling, etc. which are important aspect of image analysis aimed at dimensionality reduction. The images are downloaded on a laptop for processing using MATLAB.

Image Cropping

The first step in image pre-processing is image cropping. Some irrelevant parts of the image can be removed, and the image Region of Interest (ROI) is focused. This tool provides a user with the size information of the cropped image. MATLAB function for image cropping realizes this operation interactively by waiting for a user to specify the crop rectangle with the mouse and operate on the current axes. The output images of the cropping process are of the same class as the input image.

Naming of OPIB Ear Images

The OPIB ear images were labelled based on the naming convention formulated from this study as shown in Figure 5. The images are given unique names that specifies the subject, the side of the ear (left or right) and the angle of capture. The first and second letters (SU) in the image names is block letter simply representing subject for subject 1-to-n in the dataset, while the left and right ears is distinguished using L1, L2, L3 and R1, R2, R3 for angles 60⁰, 30⁰ and 0⁰_, respectively as shown in Table 1.

Table 1: Naming Convention for OPIB ear images

NAMING CONVENTION

Label Degrees 600 300 00

No of the degree 1 2 3

Subject 1 indicates (first image in dataset) SU1

Subject n indicates (last image in dataset) SUn

Left Image 1 L 1 Left image n L n Right Image 1 R 1 Right Image n R n

SU1L1 SU1RI SU1L2 SU1R2 SU1L3 SU1R3

OPIB EAR DATASET EVALUATION

The prominent challenges with the current evaluation practices in the field of ear biometrics are the use of different databases, different evaluation matrices, different classifiers that mask the feature extraction performance and the time spent developing framework (Abaza et al., 2013; Emeršič et al., 2017).

The toolbox provides environment in which the evaluation of methods for person recognition based on ear biometric data is simplified. It executes all the dataset reads and classification based on ear descriptors.

DESCRIPTION OF OPIB EAR DATASET

OPIB ear dataset was organised into a structure with each folder containing 6 images of the same person. The images were captured with both left and right ear at angle 0, 30 and 60 degrees. The images were occluded with earing, scarves and headphone etc. The collection of the dataset was done both indoor and outdoor. The dataset was gathered through the student at a public university in Nigeria. The percentage of female (40.35%) while Male (59.65%). The ear dataset was captured through a profession camera Nikon D 350. It was set-up with a camera stand where an individual captured in a process order. A total number of 907 images was gathered.

The challenges encountered in term of gathering students for capturing, processing of the images and annotations. The volunteers were given a brief orientation on what their ear could be used for before, it was captured, for processing. It was a great task in arranging the ear (dataset) into folders and naming accordingly.

Table 2: Overview of the OPIB Ear Dataset

Location

Both Indoor and outdoor

Sign language is a cardinal element for communication between deaf and dumb community. Sign language has its own grammatical structure and gesticulation nature. Research on SLRT focuses a lot of attention in gesture identification. Sign language comprises of manual gestures performed by hand poses and non-manual features expressed through eye, mouth and gaze movements. The sentence-level completely labelled Indian Sign Language dataset for Sign Language Translation and Recognition (SLTR) research is developed. The ISL-CSLTR dataset assists the research community to explore intuitive insights and to build the SLTR framework for establishing communication with the deaf and dumb community using advanced deep learning and computer vision methods for SLTR purposes. This ISL-CSLTR dataset aims in contributing to the sentence level dataset created with two native signers from Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India and four student volunteers from SASTRA Deemed University, Thanjavur, Tamilnadu. The ISL-CSLTR corpus consists of a large vocabulary of 700 fully annotated videos, 18863 Sentence level frames, and 1036 word level images for 100 Spoken language Sentences performed by 7 different Signers. This corpus is arranged based on signer variants and time boundaries with fully annotated details and it is made available publicly. The main objective of creating this sentence level ISL-CSLRT corpus is to explore more research outcomes in the area of SLTR. This completely labelled video corpus assists the researchers to build framework for converting spoken language sentences into sign language and vice versa. This corpus has been created to address the various challenges faced by the researchers in SLRT and significantly improves translation and recognition performance. The videos are annotated with relevant spoken language sentences provide clear and easy understanding of the corpus data. Acknowledgements: The research was funded by the Science and Engineering Research Board (SERB), India under Start-up Research Grant (SRG)/2019–2021 (Grant no. SRG/2019/001338). And also, we thank all the signers for their contribution in collecting the sign videos and the successful completion of the ISL-CSLTR corpus. We would like to thank Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India for their support and contribution.