Data from a survey of 35 students in a Norwegian high school class (mostly 17-year-olds) on 1 June 2016, by Jill Walker Rettberg, Professor of Digital Culture, University of Bergen. See http://jilltxt.net/?p=4505 for details.I gathered this data for a project on Snapchat narratives. I want to understand how stories are told on Snapchat. My main method is textual analysis, and this data is simply intended to give me a better idea of whether users actually watch Live stories and other stories, and whether they make them - and to give me some ideas for where to dig deeper as I continue researching stories. I plan to visit more high schools to get more responses, but since Snapchat's interface changed in 2016, the results won't be directly comparable.Importantly, this data was collected BEFORE the update in mid-June that made Live Stories and Discover channels look the same. I assume the numbers will change with this interface change.They survey was conducted in Norwegian. One of the images in the fileset shows the survey as administered. The other image shows a translation into English. I have translated the comments as directly as possible before transcribing them into the spreadsheet. The image of a filled out survey is a translation of the Norwegian survey the students actually filled out. The original Google spreadsheet is at https://docs.google.com/spreadsheets/d/13Z4ZdeoHAeI9zYqNw6Oa7Qs64g3873dAYZTcCWH1tyo/edit#gid=1943894532.

iSeeChange is an unprecedented national climate reporting project that uniquely combines citizen science, using public media to reach people and cutting edge wx data collection methods. Built as a crowdsourced wx almanac for a national user base, iSeeChange also connects to radio audiences on climate related issues in their daily work and play through their stories.The project will build a citizen science corps by giving participants a chance to report from their own experences and opportunities to connect to experts. User generated reports include text, photos, and stories. NASA will be a key partner in the next phase by providing SIF and CO2 data for user generated observations of related phenomena on the ground.

The FAIR Island Project for Place-based Open Science is an exploratory project that is comprehensively testing the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in an Open Science context, aiming to apply these principles from the start of data gathering. This data gathering is initially centred on the island atoll of Tetiaroa, which is northwest of Tahiti in French Polynesia, yet offers general lessons applicable anywhere. A key goal of the FAIR Island project is the development of an exemplar place-based data policy that provides the policy framework to require researchers at the field stations to not only generate Data Management Plans (DMPs) but to share research data upon completion of their projects. This story shows how new features from DataCite can be used to automatically produce an inventory of project outputs for a field station. With this aim, the FAIR Island project illustrates how to build into research practice the interoperability between DMPs and identifier systems, which includes DOIs, RORs and ORCIDs By ensuring information contained within DMPs is ‘machine-actionable,’ this can save researchers and field station managers time on project administration and allow systems to leverage DMPs as ‘living documents’ to automatically record project outputs. The FAIR Island Project is a collaboration between the California Digital Library (CDL), University of California Gump South Pacific Research Station, Berkeley Institute for Data Science (BIDS), Metadata Game Changers LLC, and DataCite. This implementation story highlights initial results and recommendations, based on a webinar for Research Data Alliance US (Robinson et al., 2021) and follow-up with the presenters.

This dataset is about book subjects. It has 1 row and is filtered where the books is The Language awareness project, years 1-3 : language for talking, living and learning. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This data collection consists of 45 biographical narrative interviews conducted by Skype and face-to-face with participants who either self-identified as asexual, or felt the term described them in some way or who experienced little or no sexual desire/attraction. Participants were sought from online communities such as Aven – the main asexuality online community. We also advertised for research participants through local LGBTQ groups, Sussex and Glasgow Universities, libraries and relevant community centres/hotspots. In total, 50 participants were interviewed and 27 people also completed a diary. 23 people filled out the diary-questionnaire. Only 45 participants agreed that their interview data could be transferred to the UK Data Archive. This project sought to understand asexual identities, lifestyles and intimate practices using a Symbolic Interactionist approach. The study focused on two research questions: What does it mean to be asexual for different types of people and how do diverse asexual identities develop over time? What are the practices of intimacy for asexual people including those in relationships with asexual or sexual partners? To answer our research questions we conducted biographical narrative interviews, collected solicited diaries and diary-questionnaires. The biographical narrative interviews explored the individual’s experience of being asexual over time; the life events, experiences and relationships that shaped how participants saw themselves and the trajectory of their lives. The participant diaries collected information about the everyday practices of intimacy i.e. the events, experiences, interactions when participants felt close/intimate with someone, a pet or anything else. The third stage of data collection involved a diary-questionnaire that asked participants about their experience of doing the diary. The data was analysed in a number of ways, some of these using the computer software program NVivo 10. This research was designed in accordance with the British Sociological Association’s ethical code. Participants were given an information sheet detailing the research and were asked to sign a consent form which acknowledged their right to terminate their participation at any point. Participants were also accorded full anonymity with any identifying characteristics removed from their transcripts e.g. names, locations occupations etc. The sampling process involved advertising for participants through online communities such as Aven.org, local LGBTQ groups in two UK cities, an article written for Huffington Post and also hard copy adverts in local libraries and community organisations. We were keen to minimise the effects of sampling from organisations such as AVEN which in other studies had produced samples skewed towards younger women, with higher levels of education, middle class, white and American (e.g. Scherrer, 2008) so we first contacted those who were male and over the age of 30 yrs. The rest of the sample was decided by a random number generator applied to the remaining group of participants. In terms of information given in our pre interview, short demographic information form, and in relation to the whole cohort of 50 people, the majority of participants normally resided in the US (n=24) and the UK (n=20), with 3 residing in Sweden, 2 in Canada and 1 in Australia. In terms of gender identification, 33 identified as female, 8 as male, 2 as agender, 2 as androgynous, 1 as female boarding on agender, 1 as male unsure, 1 as transgender/gender fluid and 2 people did not respond to the question. The sample was relatively youthful: 23 participants were 18-23 yrs, 15 were 24-29 yrs, 7 were 30-47 yrs, 2 were 48-59 yrs and we did not have data on 3 participants. Participants were also relatively well educated with highest levels of education reported being: college/university degree (n=24), A levels (n=11), completing High School (n=10), post graduate qualification (n=3) and missing data (n=2). Participants were not ethnically diverse with 39 people describing their ethnic identity as white/Caucasian/, 2 as British, 2 as Jewish, 1 as Native American, 2 as mixed race and there was not data for 4 people. The large majority of people reported their relationship status as single (n=36), 10 people reported being in relationships, 2 in committed platonic relationships, 1 was dating and one was in a polyamorous relationship.

Ballarat Open Data Ballarat Open Data is a community-based data sharing platform for the Ballarat region. It provides access and integration of a range of datasets published by a variety of local organisations and community groups. Most datasets are open and freely available for search, download in open-source formats and reuse as a community service. By bringing together datasets focused on Ballarat and facilitating exchanges across different organisations and group,s this project aims to foster new forms of digital innovation, collaboration and business and research opportunities. On this platform you can search a wide range of datasets by theme, keyword and organisation. Most datasets are available for download within the platform in a variety of common open-source formats including a CKAN API for developers. It is a core principle of this project that data and data ownership is retained by the original creators/custodians therefore some datasets with more sensitive content may have access control specified by the data providers with contact information. In addition to data access and searches, Ballarat Open Data provides a number of information dashboards, interactive portals and data stories using the available open datasets. This includes dynamic and live data feeds of smart devices and sensors from the new LoraWAN network installed throughout the City as part of the broader Ballarat Smart Cities and Suburb. These applications are provided as a resource for key use-cases of community interest and a showcase of the potential of open data. If you have been involved in a project or application that has used open data from this platform that you would like us to highlight, we would love to hear from you (contact us). Data Sources and Licences Datasets in Ballarat Open data are maintained and owned by the provider organisations. Licences for each specific dataset are included in the meta data listing. The vast majority of these are published under a Creative Commons 3 licence (https://creativecommons.org/licenses/by/3.0/au/) allowing open sharing and reuse with attribution. Project Funding and Partners Ballarat Open Data is an outcome of the Ballarat Smart Cities and Suburbs project which is a collaboration of the City of Ballarat and Federation University. Funding for this project was contributed by the Australian Government Department of Industry, Science, Energy and Resources as part of the Smart Cities and Suburbs Program. Other project partners include Ballarat Hackspace and Meshed.