The Journals dataset includes information about all historical Journal titles from Springer Nature, including ones that have been decommissioned.See also: https://scigraph.springernature.com/explorer/datasets/data_at_a_glance/A journal record usually includes information about its publisher, imprint, license model, chief editor, external identifiers, subjects and impact factor when available.Version info:* http://scigraph.downloads.uberresearch.com/archives/current/TIMESTAMP.txt* http://scigraph.downloads.uberresearch.com/archives/current/LICENSE.txt

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

This dataset consists of all of the ISA-Tab format metadata files accompanying Data Descriptors published in Scientific Data from journal launch in May 2014 to September 2019 inclusive. In the September 2020 the journal transitioned to a JSON-LD format for these metadata files.The dataset consists of a single ZIP archive containing sub-directories for each Data Descriptor's metadata. Each directory is given a unique identifier based on the publication's DOI. More information on the metadata specification can be found on the journal blog, here: http://blogs.nature.com/scientificdata/2014/01/08/scientific-datas-metadata-specification/Prior to release, all investigation files were processed to remove a number of redundant and unnecessary data fields. For this reason, they will differ slightly from the versions still hosted alongside the papers on the Scientific Data journal website. Specifically, the following fields have been removed:* Comment[Subject Keywords]

• Comment[Supplementary Information File Name]

• Comment[Supplementary Information File Type]

• Comment[Supplementary Information File URL]The tool used to perform this processing is freely available on Github: https://github.com/joeasalter/scidata-isatab-cleaner

This dataset results from 13 data description sessions conducted at U. Porto. In each session researchers have created metadata in the Dendro, research data management platform. A project for each session was created beforehand in Dendro and all the sessions were kept under the same account. All projects were kept private. This was explained to the researchers and they could have changed any information if they wanted to. When scheduling the sessions researchers were asked to choose a dataset to describe. The sessions started by introducing researchers to Dendro with a brief demonstration of its features. The researchers were then asked to create a folder and upload their datasets. During the session the selection of descriptors was mostly up to them. Exceptionally, they were asked if a given descriptor was suitable to contextualize their data. Sessions audio was recorded with the researchers’ consent and were deleted after the transcription of relevant events and comments during each session to complement the analysis of the metadata produced. The audio was also used to mark the moment the researchers started and finished the description, in order to ascertain the session duration.

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

Version 20230905: first public release of RASMI (Regional Assessment of buildings' Material Intensities).

This Zenodo version contains two files:

• MI_ranges_20230905.xlsx is the dataset of the estimated MI ranges. This is probably the file you're looking for.
• MI_data_20230905.xlsx is the raw pools of MI used to create the MI ranges. This is mostly for reproducability.

Please refer to the GitHub readme.md in https://github.com/TomerFishman/MaterialIntensityEstimator for details and how to use.

Please cite both the Data Descriptor and the specific data version used:

Data Descriptor: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven. RASMI: Global Ranges of Building Material Intensities Differentiated by Region, Structure, and Function. Scientific Data 2024, 11 (1), 418. https://doi.org/10.1038/s41597-024-03190-7.

Data version: preferably use the DOI of the Zeonodo release. Refer to the release number (on the right)

This work was conducted with support by the IIASA-Israel program, and by the Israel Science Foundation project RUSTY (grant no. 2706/19). Funding was also provided by the Horizon Europe research and innovation programme under grant agreement no. 101056868 (CIRCOMOD) for TF and grant agreement No 101056810 (CircEUlar) for AM. Opinions are those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for this. BvR and AM have been supported by the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA), and funded by the Ministry of Economy, Trade, and Industry (METI), Japan. SS was supported by the Canada Research Chair in Sustainable Infrastructure, Grant Number: 232970.

The uploaded data contains measurement data and further processed data to evaluate the quality of a LoRa network in the building NW1 of the University of Bremen. The file "LoRa_Measurement_28-04-23.csv" contains the measurement data of the signal strength per location. The other files contain data for further evaluation of the quality of the LoRa network, such as the distances between receiver and transmitter, the exact time at which the packets were sent and the reception ratio per location calculated from the measured data set. In addition to these data sets, the LaTeX files used to write the paper and the MATLAB scripts to create the heatmaps are also uploaded.

The data and paper were gathered and written as part of the "Scientific Practice" lecture at the University of Bremen.

This database contains data on material intensity of buildings as hosted on https://github.com/nheeren/material_intensity_db It accompanies a Data Descriptor article in the journal Scientific Data.

It is a database seed that can be extended for further infrastructure in the future. The initial database version was assembled (seeded) by Heeren and Fishman (submitted). The publication provides some analysis of the data and discusses a number of applications for the database.

This repository corresponds to the 1.0.2 release version: https://github.com/nheeren/material_intensity_db/releases/tag/v1.0.2

Please use the Data Descriptor article DOI when citing this dataset.

In the SANDBOX research project, we investigated the natural dynamics of the North Sea bed. As part of this research, we conducted multiple research cruises on the North Sea. The documents in this dataset explain which data was collected, when it was collected and the structure of the data repository (svn.citg.tudelft.nl/sandbox).

Research data management (RDM) includes people with different needs, specific scientific contexts, and diverse requirements. The description of data is a big RDM challenge. Metadata plays an essential role, allowing the inclusion of essential information for the interpretation of data, enhances the reuse of data and its preservation. The establishment of metadata models can facilitate the process of description and contribute to an improvement in the quality of metadata. When we talk about image data, the task is even more difficult, as there are no explicit recommendations to guide image management. Taking all of this into account, in this dataset, we present a proposal for a metadata model for image description. We also developed controlled vocabularies for some descriptors. These vocabularies aim to improve the image description process, facilitate metadata model interpretation, and reduce the time and effort devoted to data description.

This dataset consists in data from 13 multi-domain data description sessions follow-up questionnaires. Researchers from the University of Porto participated in a data description session and filled in a follow-up questionnaire to assess their interest in research data management, the usefulness of data description, among others. The questionnaire was conducted on Google Forms and the data copied to a spreadsheet, because the questionnaires were made individually taking into account the specificity of one of the questions.

This multi-city human mobility dataset contains data from 4 metropolitan areas (cities A, B, C, D), somewhere in Japan. Each city is divided into 500 meters x 500 meters cells, which span a 200 x 200 grid. The human mobility datasets contain the movement of individuals across a 75-day period, discretized into 30-minute intervals and 500-meter grid cells. Each city contains the movement data of 100,000, 25,000, 20,000, and 6,000 individuals, respectively.

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) for the four cities as supplementary information (e.g., POIdata_cityA). The list of 85 POI categories can be found in POI_datacategories.csv.

This dataset was used for the HuMob Data Challenge 2024 competition. For more details, see https://wp.nyu.edu/humobchallenge2024/

Researchers may use this dataset for publications and reports, as long as: 1) Users shall not carry out activities that involve unethical usage of the data, including attempts at re-identifying data subjects, harming individuals, or damaging companies, and 2) The Data Descriptor paper of an earlier version of the dataset (citation below) needs to be cited when using the data for research and/or commercial purposes. Downloading this dataset implies agreement with the above two conditions.

Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9

This dataset contains key characteristics about the data described in the Data Descriptor A dataset describing data discovery and reuse practices in research. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

This dataset contains the results of a literature review of experimental nutrient addition studies to determine which nutrient forms were most often measured in the scientific literature. To obtain a representative selection of relevant studies, we searched Web of Science™ using a search string to target experimental studies in artificial and natural lotic systems while limiting irrelevant papers. We screened the titles and abstracts of returned papers for relevance (experimental studies in streams/stream mesocosms that manipulated nutrients). To supplement this search, we sorted the relevant articles from the Web of Science™ search alphabetically by author and sequentially examined the bibliographies for additional relevant articles (screening titles for relevance, and then screening abstracts of potentially relevant articles) until we had obtained a total of 100 articles. If we could not find a relevant article electronically, we moved to the next article in the bibliography. Our goal was not to be completely comprehensive, but to obtain a fairly large sample of published, peer-reviewed studies from which to assess patterns. We excluded any lentic or estuarine studies from consideration and included only studies that used mesocosms mimicking stream systems (flowing water or stream water source) or that manipulated nutrient concentrations in natural streams or rivers. We excluded studies that used nutrient diffusing substrate (NDS) because these manipulate nutrients on substrates and not in the water column. We also excluded studies examining only nutrient uptake, which rely on measuring dissolved nutrient concentrations with the goal of characterizing in-stream processing (e.g., Newbold et al., 1983). From the included studies, we extracted or summarized the following information: study type, study duration, nutrient treatments, nutrients measured, inclusion of TN and/or TP response to nutrient additions, and a description of how results were reported in relation to the research-management mismatch, if it existed. Below is information on how the search was conducted: Search string used for Web of Science advanced search Search conducted on 27 September 2016. TS= (stream OR creek OR river* OR lotic OR brook OR headwater OR tributary) AND TS = (mesocosm OR flume OR "artificial stream" OR "experimental stream" OR "nutrient addition") AND TI= (nitrogen OR phosphorus OR nutrient OR enrichment OR fertilization OR eutrophication)

This dataset presents the assessment tool used to analyze 20 Data Management Plan (DMP) templates on the Argos platform, along with the pre-print of the manuscript for an article that is about to be published in the Journal Biblios of the University of Pittsburgh. The main objective of this study was to investigate the need to implement a DMP at Universidad Centroamericana José Simeón Cañas (UCA) to improve accessibility, discovery, and reuse of research. Using a qualitative case study methodology, we worked with 10 selected research groups to evaluate and adapt a base model for the DMP. The results indicated a significant improvement in research data management and a positive perception from users regarding the processing and organization of their data. This set includes the DMP format generated for UCA, as well as recommendations for other institutions interested in adopting similar data management practices, contributing to the continued growth of scholarly output and the ethical and..., Method: A qualitative case study methodology was employed, which included participant observation of researchers and administrative staff from various 2024 research groups, along with an analysis of documentation and LibGuides. A benchmarking process was also conducted, comparing 20 PGDI templates to extract the best structure and practices from various research institutions. Content analysis: This method was used to examine a set of 20 PGDI templates from the ARGOS initiative, a platform developed by OpenAIRE and EUDAT for planning and managing research data. A systematic review of the structure and content of each of these templates was conducted, assessing the clarity, consistency, and adequacy of the information presented. Through this content analysis, key elements were identified that needed to be incorporated or improved in the base template provided to UCA research groups. This process allowed us to highlight best practices and identify areas that required additional attention, ..., , # Data from: Data management plan (DMP): Towards more efficient scientific management at the Universidad Centroamericana José Simeón Cañas

https://doi.org/10.5061/dryad.1zcrjdg25

Description of the Data and File Structure

README for the Dataset: Implementation of a Data Management Plan (DMP)

Dataset Description

This dataset includes the evaluation instrument used to analyze 20 Data Management Plan (DMP) templates on the Argos platform. Additionally, the pre-print of the manuscript of the article that is set to be published in the Journal Biblios at the University of Pittsburgh has been attached. Furthermore, the format of the Data Management Plan generated for the Universidad Centroamericana José Simeón Cañas (UCA), developed from this research, is included.

Objective

The primary objective of this study was to investigate the need to implement a Data Management Plan (DMP) to improve the accessibility, discoverability...

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

We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. This is the first publicly available dataset studying the daily behaviours and engagement of high school students using heterogeneous methods. The combined data could be used to analyse the relationships between indoor climates and mental states of school students.

The detailed data descriptor has been published in Nature Scientific Data. For more details on the dataset, please check the paper. https://doi.org/10.1038/s41597-022-01347-w.

Please cite the following papers if the dataset is used in a publication:

[1] Gao, N., Marschall, M., Burry, J. , Watkins, S., Salim, F. Understanding occupants’ behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables. Sci Data 9, 261 (2022).

[2] Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020). n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-26.

The file geoc_inv.txt contains identifiers for patent first filings (corresponding to appln_id in PATSTAT), latitude, longitude, city, region, and country of the inventor. Missing coordinates have been imputed from equivalents and other second filings or from information on the location of applicants. The file also contains a variable indicating the source of information ('source'): 1: information comes from the first filing itself 2: information comes from direct equivalent 3: information comes from other subsequent filings 4: information comes from the applicant’s location in first filings 5: information comes from the applicant’s location in the equivalent 6: information comes from the applicant’s location in other subsequent filings; the column 'coord_source' indicates the source of coordinates (whether they come from geolocalisation services, from geonames, or from PatentsView). It is possible to select certain types of first filings based on column 'type'. For example, Paris Convention priority filings can be retrieved by specifying type=priority. The file geoc_app.txt contains location information of applicants. Sources of information (first filings, equivalents, etc.) are thus browsed in reverse order. A detailed data description can be found in de Rassenfosse, Kozak, Seliger 2019: Geocoding of worldwide patent data, published in 'Scientific Data' and available at https://doi.org/10.1038/s41597-019-0264-6. Please note the following: The files geoc_inv_person.txt and geoc_app_person.txt contain person IDs for inventors and applicants, respectively, whenever the location information comes from PATSTAT. If not, the person_id is = 0. These files are not described in the paper. They have been made accessible to improve interoperability with PATSTAT data. Some files had to be zipped in order to upload them to Harvard Dataverse.

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.