This dataset contains occurrence data of flora and fauna species. From the Netherlands on a 5 x 5 km scale, data from other countries are exact. Observations from Belgium are excluded and can be accessed on GBIF through Natuurpunt and Natagora. It summarizes the observations recorded by >175.000 volunteers.

This data file contains demographic information along with established scale scores of 515 both allotment and non-allotment owners to explore their time in nature and the impacts of this on mental health. Also, questions were asked around time and activities in nature. The age range was between 18-85 years old. The data had approval from the psychology research ethics committee. Before data collection began, full informed consent was obtained from participants as well as information sheets provided to them and all participants were fully debriefed afterwards. Identifying information was removed to ensure confidentiality and anonymity. The scores for the scales on mental health (Clarke et al, 2011; NHS health scotland,2008), self-esteem (Robins, Hendin & Trzesniewski, 2001), physical health (DeSalvo et al, 2006), connection to nature (Nisbet, & Zelenski,2013), social identity (Sani, Madhok, Norbury, Dugard & Wakefield, 2014), loneliness (De Jong Gierveld & Van Tilburg, 2006), social support (Van Dick & Haslam, 2012) and self- efficacy (Tambs, & Røysamb, 2014) are included.

We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change. The recording scheme implemented at nature reserves offers unique opportunities for addressing community-level change across replicate local communities. These data have been systematically collected not as independent monitoring efforts, but using a shared and carefully standardized protocol adapted for each local community. Thus, variability in observation effort is of much less concern than in most other distributed cross-taxon phenological monitoring schemes. To enable analyses of higher-level taxonomical groups, we have included taxonomic classifications for the species in the database. The compilation of the data in a common database was initiated in the context of the project “Linking environmental change to biodiversity change: long-term and large-scale data on European boreal forest biodiversity” (EBFB), funded for 2011-2015 by the Academy of Finland, and continued with the help of other funding to OO since 2016. We organized a series of project meetings that were essential for data acquisition, digitalization and unification. These meetings were organized in Ekaterinburg (Russia) by the Institute of Plant and Animal Ecology, Ural Branch of RAS (Russian Academy of Sciences) in 2011; in Petrozavodsk (Russia) by the Forest Research Institute, at the Karelian Research Center, RAS in 2013; in Miass (Russia) by the Ilmen Nature Reserve in 2014; in Krasnoyarsk (Russia) by the Stolby Nature Reserve in 2014; in Artybash (Russia) by the Altaisky Nature Reserve in 2015; in Listvyanka, Lake Baikal (Russia) by the Zapovednoe Pribajkalje Nature Reserve in 2016; in Roztochja (Ukraine) by the Ministry of Natural Resources of Ukraine in 2016; in Puschino (Russia) by the Prioksko-Terrasnyj Nature Reserve in 2017, in Vyshinino (Russia) by the Kenozero National Park in 2018, and in St Petersburg (Russia) by the Komarov Botanical Institute of the Russian Academy of Sciences in 2019. The compilation of the data into a common database was conducted by the database coordinators (EM and CL) in Helsinki (Finland). Those participants that already held the data in digital format submitted it in the original format, and those that had the data only in paper format digitized it using Excel-based templates developed in the project meetings. Submitted data were processed by the database coordinators according to the following steps: The data were formatted so that each observation (the phenological date of a particular event in a particular locality and year) formed one row in the data table (e.g. un-pivoting tables that involved several years as the columns). The phenological event names were split into event type (e.g. “first occurrence“) and species name. The event type names (provided originally typically in Russian) were translated into English and the species names (usually provided in Russian) were identified to scientific names, using dictionaries that were partly developed and verified in the project meetings. All scientific names were periodically verified by mapping them to the Global Biodiversity Information Facility (GBIF) backbone taxonomy. We associated each data record with the following set of information fields: (1) project name, i.e. the source organization, (2) dataset name, (3) locality name, (4) unique taxon identifier, (5) scientific taxon name, and (6) event type. We imported the data records in the main database (maintained as an EarthCape database at https://ecn.ecdb.io). During the import, the taxonomic names, locality names, and dataset names were matched against already existing records.

This dataset contains the PISM model output data of the Antarctic Ice Sheet hysteresis simulations published and discussed in Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. Nature 585(7826), 2020. A detailed description of the individual file contents is given in README.txt below. The corresponding PISM model code used for these simulations is archived here. In case of questions, feel free to contact me at julius.garbe@pik-potsdam.de.

Research on the benefits of nature to people: How much overlap is there in citations and terms for ‘nature’ across disciplines?

https://doi.org/10.5061/dryad.44j0zpcmk

This dataset is a list of 210 papers, all representing research into the benefits of nature to people, which we used to conduct a scoping review on this topic.

As a starting point for this scoping review, we chose four broad research areas (medicine, psychology, education and environment), selected to represent disparate approaches to research on the benefits of nature to people, within and across which to explore overlap in citations and terms used to describe nature.

We conducted expert consultation and a snowball-based approach to source publications, resulting in this sample of 210 papers, spanning multiple disciplines within each of our four research areas. For each paper, we recorded the discipline of the journal in which it was published (publishing discipline), the discipline of its first author (first-aut...

The goal of this study was to compile and analyze data about incidents of domestic violence in San Diego County, California, in order to enhance understanding of the nature and scope of violence against women. The following objectives were set to achieve this goal: (1) to develop a standardized interview instrument to be used by all emergency shelters for battered women in the region, and (2) to conduct interviews with shelter staff. For this study, the San Diego Association of Governments (SANDAG) collected information about domestic violence in San Diego County from clients admitted to battered women's shelters. The Compilation of Research and Evaluation (CORE) intake interview (Part 1) was initiated in March of 1997. Through this interview, researchers gathered data over a 22-month period, through December 1998, for 599 clients. The CORE discharge interview (Part 2) was theoretically completed at the time of exit with each client who completed the CORE intake interview in order to document the services received. However, data collection at exit was not reliable, due to factors beyond the researchers' control, and thus researchers did not receive a discharge form for each individual who had an intake form. For Part 1 (Intake Data), demographic variables include the client's primary language, and the client and batterer's age, education, race, how they supported themselves, their annual incomes, and their children's sex, age, and ethnicity. Other variables cover whether the client had been to this shelter within the last 12 months, the kind of housing the client had before she came to the shelter, person's admitted along with the client, drug and alcohol use by the client, the batterer, and the children, relationship between the client and the batterer (e.g., spouse, former spouse), if the client and batterer had been in the military, if the client or children were military dependents, the client's citizenship, if the client and batterer had any physical/mental limitations, abuse characteristics (e.g., physical, verbal, sexual, weapon involved), and the client's medical treatment history (e.g., went to hospital, had been abused while pregnant, witnessed abuse while growing up, had been involved in other abusive relationships, had attempted suicide). Additional variables provide legal information (number of times police had been called to the client's household as a result of domestic violence, if anyone in the household had been arrested as a result of those calls, if any charges were filed, if the client or batterer had been convicted of abuse), if the client had a restraining order against the batterer, how the client found out about the shelter, the number of times the client had been admitted to a domestic violence shelter, the client's assessment of her needs at the time of admittance, and the interviewer/counselor's assessment of the client's needs at the time of admittance. Part 2 (Discharge Data) provides information on services the client received from the shelter during her stay (food, clothing, permanent housing, transitional housing, financial assistance, employment, education, medical help, assistance with retrieving belongings, assistance with retrieving/replacing legal documents, law enforcement, temporary restraining order), and services this client received as a referral to another agency (attorney, divorce, child care, counseling, transportation, safety plan, victim/witness funds, mental health services, department of social services, Children's Services Bureau, help with immigration, drug treatment).

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 201...

Abstract

This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform eBird in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).

Methods

The data in this study was collected using semi-structured qualitative interviews.

Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.

Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.

Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.

Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.

Description of the data and file structure

Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.

Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.

A full list of files is provided in the README file.

Notes

This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at https://opensciencestudies.eu

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).

Human-nature connection (HNC) is a concept derived from investigating the formulation and extent of an individual’s identification with the natural world. This relationship is often characterized as an emotional bond to nature that develops from the contextualized, physical interactions of an individual, beginning in childhood. This outcome presents complexity in evaluating the development of HNC but suggests optimism in the pathways for enhancing lifelong HNC. As urban populations increase, there is a growing recognition worldwide of the potential for urban green space to cultivate HNC and thus shape the environmental identity of urban residents. The results of an online survey of 560 visitors to three community parks (managed primarily to provide a variety of physical, social and cultural opportunities) and three conservation parks (managed primarily to protect native plants and wildlife) in Madison, Wisconsin, USA, were used to investigate HNC. Linear mixed effects models evaluated v..., Methodology Study Area Madison has a population of approximately 270,000 residents, covers approximately 260 km2, and is located in south central Wisconsin, USA (US Census Bureau, 2022). Madison is currently the fastest growing city in Wisconsin and is home to the state capital and the University of Wisconsin-Madison (US Census Bureau, 2022). The study area is within the Yahara Watershed, now largely dominated by agricultural and urban land cover, and experiences four distinct seasons (Carpenter et al., 2007, Wisconsin State Climatology Office, 2010).Â
The six selected parks were based on their classification as a community or conservation park; an estimated visitation rate; a central, western, or eastern location in Madison; and approval from the Madison Parks Division of the City of Madison (Figure 1). The size of the community parks ranged from 19.07 ha to 101.50 ha, and the size of the conservation parks ranged from 24.39 ha to 39.17 ha. The parks can be broadly described as mix..., , # Human-nature connection consent form and survey

https://doi.org/10.5061/dryad.h70rxwdqr

The data set contains the raw and coded data used in the analysis as presented in the published article. The supplementary material contains two documents, the consent form that preceded the survey and the survey questions that were administered online to community and conservation park visitors in Madison, WI, USA as presented in the published article.

Description of the data and file structure

The data set contains the raw and coded data used in the analysis as presented in the published article. The supplementary material contains two documents, the consent form that preceded the survey and the survey questions that were administered online to community and conservation park visitors in Madison, WI, USA as presented in the published article.

The following provides a definition for each column notation. ParkID indicates each park's identification...

Article Processing Charges (APC) for four of the largest commercial publishers* of academic journals: Springer-Nature, Elsevier, Wiley& Sons, and SAGE collected April 28, 2021.

APCs are available in USD, EUR, and GBP where available.

Data does not include Open Access (OA) journals with no fee.

Google Sheets version

*Taylor & Francis does not maintain a price list and therefore no data was collected.

Abstract

The main objectives of the survey were: - To obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti; - To provide information on the nature and distribution of household income, expenditure and food consumption patterns; - To provide data on the household sector's contribution to the National Accounts - To provide information on economic activity of men and women to study gender issues - To undertake some poverty analysis

Geographic coverage

National, including Funafuti and Outer islands

Analysis unit

• Household
• individual

Universe

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => The household is included - do not intend to reside more than 12 months => out of scope

Kind of data

Sample survey data [ssd]

Sampling procedure

It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the island except Niulakita as it was considered too small. For selection purposes, each island was treated as a separate stratum and independent samples were selected from each. The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

For details please refer to Table 1.1 of the Report.

Sampling deviation

Only the island of Niulakita was not included in the sampling frame, considered too small.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three main survey forms used to collect data for the survey. Each question are writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaires were designed based on the 2004 survey questionnaire.

HOUSEHOLD FORM - composition of the household and demographic profile of each members - dwelling information - dwelling expenditure - transport expenditure - education expenditure - health expenditure - land and property expenditure - household furnishing - home appliances - cultural and social payments - holydays/travel costs - Loans and saving - clothing - other major expenditure items

INDIVIDUAL FORM - health and education - labor force (individu aged 15 and above) - employment activity and income (individu aged 15 and above): wages and salaries, working own business, agriculture and livestock, fishing, income from handicraft, income from gambling, small scale activies, jobs in the last 12 months, other income, childreen income, tobacco and alcohol use, other activities, and seafarer

DIARY (one diary per week, on a 2 weeks period, 2 diaries per household were required) - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling)

Questionnaire Design Flaws Questionnaire design flaws address any problems with the way questions were worded which will result in an incorrect answer provided by the respondent. Despite every effort to minimize this problem during the design of the respective survey questionnaires and the diaries, problems were still identified during the analysis of the data. Some examples are provided below:

Gifts, Remittances & Donations Collecting information on the following: - the receipt and provision of gifts - the receipt and provision of remittances - the provision of donations to the church, other communities and family occasions is a very difficult task in a HIES. The extent of these activities in Tuvalu is very high, so every effort should be made to address these activities as best as possible. A key problem lies in identifying the best form (questionnaire or diary) for covering such activities. A general rule of thumb for a HIES is that if the activity occurs on a regular basis, and involves the exchange of small monetary amounts or in-kind gifts, the diary is more appropriate. On the other hand, if the activity is less infrequent, and involves larger sums of money, the questionnaire with a recall approach is preferred. It is not always easy to distinguish between the two for the different activities, and as such, both the diary and questionnaire were used to collect this information. Unfortunately it probably wasn?t made clear enough as to what types of transactions were being collected from the different sources, and as such some transactions might have been missed, and others counted twice. The effects of these problems are hopefully minimal overall.

Defining Remittances Because people have different interpretations of what constitutes remittances, the questionnaire needs to be very clear as to how this concept is defined in the survey. Unfortunately this wasn?t explained clearly enough so it was difficult to distinguish between a remittance, which should be of a more regular nature, and a one-off monetary gift which was transferred between two households.

Business Expenses Still Recorded The aim of the survey is to measure "household" expenditure, and as such, any expenditure made by a household for an item or service which was primarily used for a business activity should be excluded. It was not always clear in the questionnaire that this was the case, and as such some business expenses were included. Efforts were made during data cleaning to remove any such business expenses which would impact significantly on survey results.

Purchased goods given away as a gift When a household makes a gift donation of an item it has purchased, this is recorded in section 5 of the diary. Unfortunately it was difficult to know how to treat these items as it was not clear as to whether this item had been recorded already in section 1 of the diary which covers purchases. The decision was made to exclude all information of gifts given which were considered to be purchases, as these items were assumed to have already been recorded already in section 1. Ideally these items should be treated as a purchased gift given away, which in turn is not household consumption expenditure, but this was not possible.

Some key items missed in the Questionnaire Although not a big issue, some key expenditure items were omitted from the questionnaire when it would have been best to collect them via this schedule. A key example being electric fans which many households in Tuvalu own.

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the NSO staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. - after data entry, outliers were identified in order to check their consistency.

All data entry, including editing, edit checks and queries, was done using CSPro (Census Survey Processing System) with additional data editing and cleaning taking place in Excel.

The staff from the CSD was responsible for undertaking the coding and data entry, with assistance from an additional four temporary staff to help produce results in a more timely manner.

Although enumeration didn't get completed until mid June, the coding and data entry commenced as soon as forms where available from Funafuti, which was towards the end of March. The coding and data entry was then completed around the middle of July.

A visit from an SPC consultant then took place to undertake initial cleaning of the data, primarily addressing missing data items and missing schedules. Once the initial data cleaning was undertaken in CSPro, data was transferred to Excel where it was closely scrutinized to check that all responses were sensible. In the cases where unusual values were identified, original forms were consulted for these households and modifications made to the data if required.

Despite the best efforts being made to clean the data file in preparation for the analysis, no doubt errors will still exist in the data, due to its size and complexity. Having said this, they are not expected to have significant impacts on the survey results.

Under-Reporting and Incorrect Reporting as a result of Poor Field Work Procedures The most crucial stage of any survey activity, whether it be a population census or a survey such as a HIES is the fieldwork. It is crucial for intense checking to take place in the field before survey forms are returned to the office for data processing. Unfortunately, it became evident during the cleaning of the data that fieldwork wasn?t checked as thoroughly as required, and as such some unexpected values appeared in the questionnaires, as well as unusual results appearing in the diaries. Efforts were made to indentify the main issues which would have the greatest impact on final results, and this information was modified using local knowledge, to a more reasonable answer, when required.

Data Entry Errors Data entry errors are always expected, but can be kept to a minimum with

RT-PCR data of comparative viral loads/ tissue levels in ferrets exposed to either NiV-MY or NiV-BD - data in the form of spreadsheets of calculated NiV copies per sample per animal and outcomes of REML analysis.

Public understanding and support are essential for managing coastal zones because these are social-ecological systems (SES) in which the public plays a crucial role. As disconnection from nature may be a root cause of the unsustainability of SES, reconnecting people to nature is a promising avenue for improving their understanding and support. Although environmental education that involves exposure to nature has been considered influential in reconnecting people with nature, empirical research is lacking. Therefore, this study aimed to assess the impact of an on-site fish workshop on Japanese elementary and junior high school student’s knowledge, attitudes, intentions, and behaviors through the notion of human-nature connection (HNC) and leverage points. A 2×2 difference-in-differences design was employed in which the workshop’s impact was assessed by comparing the treatment and control groups before and after the workshop. We collected 4,054 responses, with 1,243 (pre-) and 1,088 (post-) students in the treatment group and 857 (pre-) and 866 (post-) in the control group. The preliminary findings indicate that the workshop had diverse impacts, from shallow (parameters) to deep leverage points (Information flows, Rules, Goals, and Paradigms), including HNC, support for ongoing management measures, and pro-SES attitudes and intentions. Their diverse impacts in the same direction (i.e., improvements), as found in our study, are critical because leverage points should be aligned for systemic sustainability transformation. However, changes to leverage points measured in the average treatment effect on the treated (ATET) varied from limited to extensive. Future research directions are discussed based on the preliminary findings.

This dataset includes the survey data that were collected during the Marine Nature Conservation Review (MNCR) between 1987 and 1998, together with data from surveys commissioned by the Nature Conservancy Council in the 1970s and 1980s and data collected subsequently by JNCC. The MNCR was initiated to provide a comprehensive baseline of information on marine habitats and their associated species around the coast of Britain which would aid coastal zone and sea-use management and to contribute to the identification of areas of marine natural heritage importance. The focus of MNCR work was on benthic habitats (often referred to as 'biotopes') in intertidal and inshore (typically within 3nm) subtidal areas.

Taiwan Life Insurance: Reserve for Insurance Contracts with Nature of Financial Products data was reported at 15.471 NTD bn in 2017. This records a decrease from the previous number of 34.063 NTD bn for 2016. Taiwan Life Insurance: Reserve for Insurance Contracts with Nature of Financial Products data is updated yearly, averaging 151.804 NTD bn from Dec 2012 (Median) to 2017, with 6 observations. The data reached an all-time high of 197.292 NTD bn in 2012 and a record low of 15.471 NTD bn in 2017. Taiwan Life Insurance: Reserve for Insurance Contracts with Nature of Financial Products data remains active status in CEIC and is reported by Taiwan Insurance Institute. The data is categorized under Global Database’s Taiwan – Table TW.RG011: Life Insurance: Balance Sheet.

What do you see? This map shows the above-ground carbon stock recorded in the vegetation of nature reserves in the Netherlands. On the map are the nature reserves in the Netherlands that are covered by the Nature and Landscape subsidy scheme (SNL). What’s the value? Governments, nature managers and other interested parties for carbon sequestration will gain insight into the spread of carbon sequestration in nature. This information can be used when drawing up policy plans and other strategic planning. In addition, this map forms the basis for further studies/cards that determine potential carbon sequestration in the Dutch vegetation. Who is this important to? This map is important for policy makers, field managers and researchers involved in climate mitigation (carbon capture).

This is the dataset associated with a daily diary study designed to explore the stylistic quality of people’s reflective self-talk and the effects of those qualities on emotion immediately following that self-talk. Participants engaged in reflective self-talk each day about one positive and one negative event from that day. Participants rated the emotional intensity of each daily event which served as the inspiration for an occasion of self-talk. Occasions of self-talk were rated on the interpersonal circumplex octants with a novel measure created for this study (OLIPS), both by participants themselves and later by independent raters. Participants also reported their positive and negative affect following each instance of self-talk (I-PANAS-SF; Thompson, 2007). Participants also completed a number of trait-level measures either before the diary study or on a follow-up visit after the study: Interpersonal Self-Talk Scale (Price, 2015) Forms of Self-Criticising/Attacking & Self-Reassuring Scale (FSCRS; Gilbert et al., 2004) Revised Interpersonal Adjective Scales (IAS-R; Wiggins, Trapnell, & Phillips, 1988) Neuroticism Scale from the Big Five Version of the Revised Interpersonal Adjectives Scales (IASR-B5; Trapnell & Wiggins, 1990) Data was collected from university undergraduates in southern Ontario, Canada. The data set is associated with the following paper: Lefebvre, J.P., Sadler, S., Hall, A., & Woody, E. (2022). The interpersonal nature of self-talk: Variations across individuals and occasions. Journal of Personality and Social Psychology: Personality Processes and Individual Differences. https://doi.org/10.1037/pspp0000405

1. Numerous studies have shown the positive association between nature engagement and well-being. During the early phases of the SARS-CoV-2 pandemic, nature engagement changed dramatically as mental health and well-being declined across the globe.

2. This study examines how psychological connection to nature and engagement with nature in various forms is associated with well-being during the SARS-CoV-2 pandemic. Specifically, we examine which types of nature engagement (i.e., with nearby nature, through nature excursions, and media-based) are more strongly associated with well-being based on measures of loneliness, rumination, pandemic emotional impact, and mental health.

3. We employed a cross-sectional online survey of adults (N=3,282) residing in the United States, 25% of whom report seldom spending time in nature.

4. Our findings revealed that the psychological construct of connection to nature was associated with less loneliness and greater mental health. Overall, nature engagement was a consistent predictor of well-being, but different types of activities predicted varying outcomes on our four dependent variables. Greater engagement with nearby nature during the pandemic was associated with less rumination, less pandemic emotional impact, and better mental health while nature excursions (e.g., camping, backpacking) and media-based nature engagement were associated with greater loneliness, more emotional impact from the pandemic, and worse mental health. Additionally, nature engagement via media was associated with greater rumination.

5. Our findings suggest that promoting opportunities to increase engagement with and access to nearby nature is associated with better human well-being, especially during challenging events, and should be part of a multi-pronged approach for coping with the next public health crisis.

Updated yearly using enrollment data, employment data, information from websites, phone calls, and any other resources as available. At time of update fields were added to include employment data, enrollment data, building code, school code, TAZ08, and school website. Please verify information before use as it will be updated on an ongoing basis. Please contact COMPASS with any questions or any knowledge of updates, alterations or modifications that need to be made. FIELDS:UpdateBy: Name or initials of last person to update the recordUpdateOn: Date the record was last updated onSchoolName: Name of the school at the pointSchoolDist: School district the point physically is withinType: Describes the nature of the building and grade/age range of students enrolledValues:PRE K: Preschool &/or Nursery school & Day CareELEMENTARY: Traditional Kindergarten through 6thgradeK-8: Kindergarten through 8th gradeK-12: Kindergarten through 12th grade MIDDLE: 6thgrade through 8thgradeJUNIOR HS: 7thgrade through 9th gradeSENIOR HS: 9th through 12thgradePOST SR: College, University, Technical or Professional SchoolsOTHER: Irregular range of grades or ages ADMIN: Administrative Building/ServicesRETAIL-EDU: Retailor or seller of educational materials or suppliesSiteAddres: Physical address of the school or buildingSiteCity: City the school or building is located inSiteState: State the school or building is located inSiteZip: Zip code the school or building is located inSiteCounty: County the school or building is located inBuilding_Code: Building Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_Code:School Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_JoinID: Concatonated field of Building Code + School Code as a 7 digit code assigned by the 2012 Enrollment data sheet. If the School Code is only a three digit code an additional ‘0’ is added before the code to achieve the full seven digits necessary for the field. Where the number is not available or this does not apply the value used is ‘N/A’Notes: Any pertinent information that was not suited for another fieldEmploy13:Number of employees according to the 2013 employment final point fileTAZ08: TAZ08 in which the point liesType_II:Describes the nature of the school – public vs private runValues:PUBLIC: Owned, operated, funded, governed and sanctioned by the Idaho Department of EducationPRIVATE: Owned, operated & funded by private donors, foundation, trust or other source. May or may not meet State or Federal curriculum requirements/standardsOPT_ENROLL: Y/N field indicating if there is an open enrollment boundary for the schoolType_III:Any further information or description about the school. Values:AG PRODUCTION & RESEARCH: U of I extension campuses with specific research focus and use intentionALTERNATIVE: Any alternative learning environment, field may contain a ‘-_’ for a further description about what the alternative style is; teen parents, night school, at risk, ect…CHARTER: Any public school classified as a charter by the State Board of EducationCOLLEGE, UNIVERSITY, TRADE SCHOOL: Any post-secondary education institution, includes graduate programs, law schools and vocational training programs.COMMUNITY EDUCATION – ENVIRONMENTAL: Nontraditional classroom facilities which offer courses for the community (child and adult) to promote higher learning and understanding of the environment, care of the environment and environmental issues.CULTURAL: Any school which offers cultural enrichment or a multi-cultural learning environment. Field may also contain ‘-_’ to describe what the specific culture the school educates in.DURRING INCARCERATION: Schools are run through the Juvenile Detention Centers. These schools are acknowledged by the State Department of Education, and are recognized by the State. Available to students during the time of their incarceration. FAITH BASED: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum.FAITH BASED BOARDING: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum. These school also offer a live in facility option to students.HEADSTART: Formal pre-kindergarten education programsINTERNATIONAL BACCALAUREATE: School which offers programs for International Baccalaureate credit for studentsLANGUAGE AND CULTURE: Private (non-charter) language and culture focused schools. Field may also contain ‘-_’ to describe what the specific culture the school educates in.MAGNET: Any school with a particular subject area focus intended to draw students with natural aptitudes or specific interests, these schools have open enrollment boundaries with an application process, as long as the student resides within the school district to which the school is a part of. MONTESSORI: Private schools with a focus on experiential learning rather than traditional learning methods. MUSIC: Schools with an additional focus on musical aptitude and methodsONLINE OR HOME SCHOOL: Virtual or online classroom optionsSPECIAL NEEDS: Schools with facilities and resources for students with special needs or additional assistance and attention. Access: Indicates whether the point is the actual building location itself or an access point. Building locations are coded as "Loc" and access points are coded as "PV" for pedestrian/vehicle access.Main_Acc: Identifies if an access point is the main entrance/exit location for each school.Source: Where the numbers for the employment data and/or student enrollment were gathered from.Enrollment: # of students enrolled according to the 2012 enrollment data, or based on best information we were otherwise able to obtain (if not on the 2012 enrollment data).Website:Most recent URL if able to locate, if unable to locate indicated in field with “UTL”Status: Used to describe if the school is currently active, closed, or planned (used to query out inactive schools for performance monitoring purposes)UniqueID: Made by combining District number and building number in from DDDBBBB. _Updated Fall 2013 From School District WebsitesUpdated 9/11/11 From School District WebsitesJuly 2010 . Canyon County has since requested a new data structure to match their address points. The new schools file has the new structure. The point location of this file is identical to the new schools point file May 2010 - Edited the Ada County schools to align with school sites on NAIP imagery and confirmed schools against respective school district websites Jan - March 2010 - Worked with Jay Young over a several month period and several renditions to reconcile the Canyon County side of this file. December 2009 - Merged with Jay Young's Canyon point file in order to build a new data structure that meets Emergency Service data standards. Went through point by point to ensure alignement with buildings on NAIP imagery and attribute values.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel