This dataset presents the quantitative raw data that was collected under the H2020 RRI2SCALE project for the D1.4 - “Large scale regional citizen surveys report”. The dataset includes the answers that were provided by almost 8,000 participants from 4 pilot European regions (Kriti, Vestland, Galicia, and Overijssel) regarding the general public's views, concerns, and moral issues about the current and future trajectories of their RTD&I ecosystem. The original survey questionnaire was created by White Research SRL and disseminated to the regions through supporting pilot partners. Data collection took place from June 2020 to September 2020 through 4 different waves – one for each region. Based on the conclusion of a consortium vote during the kick-off meeting, it was decided that instead of resource-intensive methods that would render data collection unduly expensive, to fill in the quotas responses were collected through online panels by survey companies that were used for each region. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the "Large scale regional citizen surveys report" (D1.4).

Here we present an anonymized version of the dataset that we collected in the quantitative phase of Wellcome’s research on research culture. Additionally, we present a document detailing how the data was transformed to protect anonymity. We also present a flowchart that indicates how participants were guided to answer questions in the survey.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

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

The dataset appears to be a collection of text entries with associated emotional responses and metadata. Here are some insights:

Columns:

The dataset includes columns such as text, condition, recalled, slur_source, slur_gender, subj_anger, f_pain, f_fear, f_panic, f_anger, f_guilt, and f_humiliation.

Entries: There are 504 entries in total.

Types of Data: The data types include text, integers, and floats, indicating a mixture of qualitative and quantitative data.

The Diary Study (also known as The T.E.A.M. Study or The Progress Principle Study) was carried out in the late 1990s to early 2000s in order to probe the everyday work experiences of professionals working on important innovation projects within their companies. Teresa Amabile was the principal investigator. The database contains quantitative data and detailed categorical coding of qualitative data (not the verbatim qualitative data itself). Data were collected daily from the 238 professionals in 26 project teams who participated in this study throughout the entire course of a project (or discrete project phase) that required creativity – novel, useful ideas – in order to be successful. Many of the projects involved new product development. To the extent possible, daily data collection with a given team began on the first day of the project and continued until the last day. A large body of additional data on the individuals and their performance was collected at various other points during the study. The 26 teams were recruited from seven different companies in three industries: high tech, chemicals, and consumer products. Five of the companies had four teams that participated; one company had five teams; and one company had one team. (Please see the Metadata tab, below, for full description.) The primary source of data is the Daily Questionnaire (DQ) diary form that each participant was emailed each workday, Monday through Friday, throughout the course of the project on which the participant’s team was working. Participants were asked to return the completed diary, which took most people 5-10 minutes to complete, shortly before the end of their workday. Most did complete the diary on the day that the diary referred to, but some habitually completed the diary early the next day. The overall response rate was 75%, yielding a total of 11,637 individual daily diary entries. The DQ, which was identical for each day, contained questions calling for Likert-scale responses to questions about psychological state that day: (a) emotions; (b) motivation; and (c) perceptions of the project supervisor, the project team, the work environment, and the work itself. In addition, participants completed an open-ended question asking them to describe one event that stood out in their minds from the day that was relevant to the work in some way – the “Event Description” (ED) – and then answered additional Likert-scale questions about the event. The DQ included a few additional quantitative items. Although the DQ forms collected both quantitative and qualitative data (the EDs), the raw qualitative data are not included in this database. All included data have been de-identified, and it was not possible to adequately disguise the qualitative data. However, this database contains codes from several different coding schemes that prior researchers using this database created to categorize the events (and attributes of events) that participants reported in their EDs. Of the two primary coding schemes, the Detailed Event Narrative Analysis (DENA) scheme is extremely detailed; the Broad Event Narrative Analysis (BENA) scheme is considerably less detailed. In addition, several LIWC (Linguistic Inquiry and Word Count) analyses of the EDs are included in this database. A great deal of additional quantitative data was collected from all participants at various points in the study of their teams, including: demographics; personality; job satisfaction; cognitive style; motivational orientation; broad perceptions of the work environment, the project team, and the project; and monthly assessments of the performance of themselves and each of their teammates. Data were also collected from multiple managers in the participant’s area of the organization, who were broadly familiar with projects in that area. These managers completed monthly surveys assessing each of the participating projects, as well as a set of comparable but non-participating projects, on several dimensions. The book, The Progress Principle (Amabile, T. & Kramer, S., 2011, Harvard Business Publishing), reports a number of findings derived from quantitative and qualitative analyses of this database. The Research Appendix of this book contains descriptions (written in non-technical terms) of the Diary Study companies, participants, procedure, data collection instruments, data, and primary analyses conducted by Amabile and her colleagues. The Dataverse record lists several papers that used this database. Like the book, they can be used for additional information about the data collection methods and instruments as well as findings. Approval is required for use of this data. To apply for access, fill out the Diary Study application for use; make sure first you already have a Dataverse account.

This dataset provides the raw anonymised (quantitative) data from the EDSA demand analysis. This data has been gathered from surveys performed with those who identify as data scientists and manages of data scientists in different sectors across Europe. The coverage of the data includes level of current expertise of the individual or team (data scientist and manager respectively) in eight key areas. The dataset also includes the importance of the eight key areas as capabilities of a data scientist. Further the dataset includes a breakdown of key tools, technologies and training delivery methods required to enhance the skill set of data scientists across Europe. The EDSA dashboard provides an interactive view of this dataset and demonstrates how it is being used within the project. The dataset forms part of the European Data Science Academy (EDSA) project which received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No 643937. This three year project ran/runs from February 2015 to January 2018. Important note on privacy: This dataset has been collected and made available in a pseudo anonymous way, as agreed by participants. This means that while each record represents a person, no sensitive identifiable information, such as name, email or affiliation is available (we don't even collect it). Pseudo anonymisation is never full proof, however the projects privacy impact assessment has concluded that the risk resulting from the de-anonymisation of the data is extremely low. It should be noted that data is not included of participants who did not explicitly agree that it could be shared pseudo anonymously (this was due to a change of terms after the survey had started gathering responses, meaning any early responses had come from people who didn't see this clause). If you have any concerns please contact the data publisher via the links below.

The use of the microscope to assess cell viability is an inherently qualitative and subjective process. This project demonstrates a new absorbance microscopy modality for quantitative single-cell assessment of cell viability via trypan blue staining. This dataset consists of .tif images of live and dead stained and unstained Jurkat cells as well as the numerical data related to the corresponding journal article. The images were collected via brightfield microscopy through a 610nm bandpass filter. The data were collected to demonstrate a new imaging modality that enables the collection of traceable and comparable images of cells over time on the same microscope or on different microscopes. The processing of the brightfield images into absorbance images allows the intracellular uptake of trypan blue measured as moles/cell or mmol/L to be determined for individual cells. In this way, measurements of cell viability can be made in a quantitative, reproducible fashion. Quantitative measurements of cell viability are greatly needed to improve consistency in the biomanufacturing of cells and cell-based therapeutics. Note about Downloading: Please note that the pathlength for the download location must not exceed 260 characters; this is the pathlength limit in Windows. If you experience an error message about pathlength, then you can create a shorter pathlength for the download. For example, you could create a folder on C drive named 000 (triple zero) which would sort to the top of the list and would be C:\000 (6 characters). REFERENCE 1. Babakhanova G, Zimmerman SM, Pierce LT, Sarkar S, Schaub NJ, Simon Jr CG (2021) Quantitative, Traceable Determination of Cell Viability Using Absorbance Microscopy. Manuscript in preparation. 2. Babakhanova G, Zimmerman SM, Simon Jr CG (2021), Dataset for AbsorbanceQ App for Generating Absorbance Images from Brightfield Image Captures, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2423 (accessed June 22, 2021) 3. Zimmerman SM, Simon Jr CG, Babakhanova G (2021) AbsorbanceQ App for Generating Absorbance Images from Brightfield Image Captures. Manuscript in preparation.

This dataset contains Dryas octopetala quantitative data collected for the Toolik Snowfence Experiment from 1996 to 2001 at the dry heath site. In each plot, the following were measured: number of flowers and fruits, length of the longest six leaves and length of all the pedicels within the plot. For more information, please see the readme file.

The purpose of the the data collection was to determine the shape of the change in beauty in depicted faces over time. The dataset is comprised by almost 120,000 paintings from different times and styles.

Quantitative public financial management research focused on local governments is limited by the absence of a common database for empirical analysis. While the U.S. Census Bureau distributes government finance data that some scholars have utilized, the arduous process of collecting, interpreting, and organizing the data has led its adoption to be prohibitive and inconsistent. In this article we offer a single, coherent resource that contains all of the government financial data from 1967-2012, uses easy to understand natural-language variable names, and will be extended when new data is available.

A quantitative dataset collected from an established oil and gas company in Europe. Survey-based research was developed based on institutional theory and carried out among the company’s employees. Our survey helps understand the process of legitimating new sustainable technologies (renewable energy) in an established oil and gas company. The data was used in two articles. The first article introduces and validates a measure of a company’s institutional profile for sustainability, develops a model, tests the model fit and validates the measures. The second article develops a model that focuses on the drivers, barriers and strategy selection criteria that lead the company to invest in a technology that is outside its core business.

Quantitative dataset of the Site 'Bassari Country' collected by Anna Porcuna in Senegal. This dataset was collected in the context of the ERC funded project: LICCI - Local Indicators of Climate Change Impacts (The contribution of local knowledge to climate change research). It includes the 2st - quantitative part of a 2 part dataset. Quantitative data collection includes household-level surveys with up to 125 randomly selected households and individual-level surveys of up to 175 individuals chosen by convenience sampling. More information on the project at https://licci.eu

Data collected from 626 postgraduate students from a research university in Malaysia. It is used for second-order analysis PLS-SEM. The participation of all students was completely voluntary. Consent was obtained prior to the collection data.

The Detection of Crime, Resource Deployment, and Predictors of Success: A Multi-Level Analysis of Closed-Circuit Television (CCTV) in Newark, NJ collection represents the findings of a multi-level analysis of the Newark, New Jersey Police Department's video surveillance system. This collection contains multiple quantitative data files (Datasets 1-14) as well as spatial data files (Dataset 15 and Dataset 16). The overall project was separated into three components: Component 1 (Dataset 1, Individual CCTV Detections and Calls-For-Service Data and Dataset 2, Weekly CCTV Detections in Newark Data) evaluates CCTV's ability to increase the "certainty of punishment" in target areas; Component 2 (Dataset 3, Overall Crime Incidents Data; Dataset 4, Auto Theft Incidents Data; Dataset 5, Property Crime Incidents Data; Dataset 6, Robbery Incidents Data; Dataset 7, Theft From Auto Incidents Data; Dataset 8, Violent Crime Incidents Data; Dataset 9, Attributes of CCTV Catchment Zones Data; Dataset 10, Attributes of CCTV Camera Viewsheds Data; and Dataset 15, Impact of Micro-Level Features Spatial Data) analyzes the context under which CCTV cameras best deter crime. Micro-level factors were grouped into five categories: environmental features, line-of-sight, camera design and enforcement activity (including both crime and arrests); and Component 3 (Dataset 11, Calls-for-service Occurring Within CCTV Scheme Catchment Zones During the Experimental Period Data; Dataset 12, Calls-for-service Occurring Within CCTV Schemes During the Experimental Period Data; Dataset 13, Targeted Surveillances Conducted by the Experimental Operators Data; Dataset 14, Weekly Surveillance Activity Data; and Dataset 16, Randomized Controlled Trial Spatial Data) was a randomized, controlled trial measuring the effects of coupling proactive CCTV monitoring with directed patrol units. Over 40 separate four-hour tours of duty, an additional camera operator was funded to monitor specific CCTV cameras in Newark. Two patrol units were dedicated solely to the operators and were tasked with exclusively responding to incidents of concern detected on the experimental cameras. Variables included throughout the datasets include police report and incident dates, crime type, disposition code, number of each type of incident that occurred in a viewshed precinct, number of CCTV detections that resulted in any police enforcement, and number of schools, retail stores, bars and public transit within the catchment zone.

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

Quantitative dataset of the Site 'Timucuy' collected by Yolanda Lopez-Maldonado in Mexico. This dataset was collected in the context of the ERC funded project: LICCI - Local Indicators of Climate Change Impacts (The contribution of local knowledge to climate change research). It includes the 2st - quantitative part of a 2 part dataset. Quantitative data collection includes household-level surveys with up to 125 randomly selected households and individual-level surveys of up to 175 individuals chosen by convenience sampling. More information on the project at https://licci.eu

Files include the survey, consent text, and dataset (partial data after removal of demographic and educational background variables for de-identification purposes) from a tracer study for the Q4D Lab (q4dlab.org) programs. Link to publication with full methods and results from the study will be added once available. The online version of the questionnaire used to collect the data can also be viewed here (note that any 3-digit code will provide access).

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

By [source]

About this dataset

This dataset explores the potential relationship between art presence and property prices in London neighborhoods. We conducted an analysis to investigate this by measuring the proportion of Flickr photographs with the keyword ‘art’ attached. We then compared that data to residential property price gains for each Inner London neighborhood, seeking out any associations or correlations between art presence and housing value. Our findings demonstrate the impact of aesthetics on neighborhoods, illustrating how visual environment influences socio-economic conditions. With this dataset, we aim to show how online platforms can be leveraged for quantitative data collection and analysis which can visualize these relationships so as to better understand our urban settings

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How to use the dataset

This dataset can be used to investigate the relationship between art presence and property prices in London neighborhoods. The dataset includes three columns – Postcode.District, Rank.Mean.Change, and Proportion.Art.Photos – which provide quantitative analyses of the association between art presence and price gains for London neighborhoods.

To use this dataset, first identify the postcode district for which you wish to access data by referencing a street list or PostCodeSearcher website that outlines postcodes for each neighborhood in London(http://postcodesearcher.com/london). This will allow you to easily find properties within each neighborhood as there are specific postcode districts that demarcate boundaries of particular areas (for example W2 covers Bayswater).

Once you have identified a postcode district of interest, review the ‘Rank.Mean Change’ column to explore how residential property prices have changed relative to other areas in Inner London since 2010-13 using fractions (1 = highest gain; 25 = lowest gain). Focusing on one particular location will also provide an idea about their current pricing level compared with others in order to evaluate whether further investment is worthwhile or not based on its past history of growth rates . It is important to note that higher rank numbers indicate higher price gains while lower rank numbers indicate lower price gains relative with respect from 2010-13 timeframe therefore comparing these values across many neighborhoods gives an indication as what area offers more value growth wise over given time period..

Finally pay attention how much did art contributes as far change in property price goes? To answer this question , review ‘Proportion Art Photos’ column which provides ratio of Flickr photographs associated with keyword 'art' attached within given regions helps identify visual characteristics within different localities.. Comparing proportions across various locations provide detail information regarding how much did share visual aesthetic characterstics impacts change in pricings accross different region.. For example it can give us further understandings if majority photographs are made up of urban landscape , abstracts or simply portrait presences had any role play when we look at relativity gains over past few years? Such comparisons help inform our understanding about potential impact art presence can have on changes stay relatively stable even during volatile market times..

By combining this data with other datasets related to demographics, infrastructure and socioeconomics present within londons different areas we can gain further insight which then allows us making informed decisions when it comes investing particular locations .

Research Ideas

• Use this dataset to develop a predictive analytics model to identify areas in London most likely to experience an increase in residential property prices associated with the presence of art.
• Use this dataset to develop strategies and policies that promote both artistic expression and urban development in Inner London neighborhoods.
• Compare the presence of art across inner London boroughs, as well as against other cities, to gain insight into the socio-economic conditions related to the visual environment of a city and its impact on life quality for citizens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/publicd...

Quantitative dataset of the Site 'Qaanaaq' collected by Leneisja Jungsberg in Greenland. This dataset was collected in the context of the ERC funded project: LICCI - Local Indicators of Climate Change Impacts (The contribution of local knowledge to climate change research). It includes the 2st - quantitative part of a 2 part dataset. Quantitative data collection includes household-level surveys with up to 125 randomly selected households and individual-level surveys of up to 175 individuals chosen by convenience sampling. More information on the project at https://licci.eu