cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25397/eur.28123376
title: Data set of "Falling and failing (to learn)"

description: Data set of "Falling and failing (to learn): Evidence from a Nation-Wide Cybersecurity Field Experiment with SMEs"Accepted for publication in the Journal of Economic Behavior and Organization Abstract:Prior experiences are crucial in shaping risk prevention behavior. Previous studies have shown that experiencing a simulated phishing attack (a ``phishing drill") reduces the likelihood of clicking on unsafe links and disclosing one's password. In a large field experiment involving 670 small and medium-sized enterprises (SMEs) and their 33,000 employees, we examined the impact of experience on individuals' ability to detect cyber-security threats, and whether this effect persisted over several months. We collected data at both the company and individual levels, including risk preference, time preference, and trust. Our findings indicate only a non-systematic, short-term effect of previous phishing emails on clicking behavior. A cluster of individuals with greater patience, trust, and risk seeking was more likely to click on phishing links in the first place but then also more likely to benefit from phishing drills.
DOI: 10.15468/dl.aznebw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melanaphis sorghi",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054930-251009101135966/datasets/export for details.
DOI: 10.25397/eur.28314236
title: Patient-relevance of outcome measures in breast cancer clinical trials - Data Files

description: The primary research question for which these data have been used, was: ‘How patient-relevant are outcomes measured in clinical trials for breast cancer drugs?’. Subquestions were: 1. Which treatment outcomes are relevant for breast cancer patients? 2. Which outcome measures are used in clinical trials for breast cancer drugs? 3. How much overlap is there between patient-relevant outcomes and outcomes measured in clinical trials?The dataset has been used to answer subquestion 2. Data have been obtained by searching Clinicaltrials.gov for trials conducted between January 2014 and March 2024 inclusive. Further inclusion criteria were that studies had to be phase III trials and had to focus on breast cancer, adults (18-64 years old) and drugs. Interventions focusing on lifestyle changes, Chinese medicine, anaesthesia, surgery and diagnostic methods were excluded. Ultimately, 264 trials were included and forty-five excluded. To determine the outcome measures used, the study plan of every included trial was reviewed and recorded on the data sheet.
DOI: 10.6084/m9.figshare.27628344
title: Enriched Tourism Dataset Paris (POIs)

description: Please, cite the following publication:----R. Hermoso, S. Ilarri, R. Trillo-Lado, & C. Marzo (2017). Recommending Needles in a Haystack: The SURGE Approach. <i>International Journal of Geographical Information Systems, 2025</i>.---This dataset contains the Paris subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/paris-attraction.csv). The original dataset comprises 4,351 entries that encompass a variety of attractions across Paris, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 477 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of Paris' attractions.- <b>Paris.csv</b>It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.- <b>Paris_annotated.csv</b>It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories.It has the following columns:* POI name* POI's address* One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.
DOI: 10.6084/m9.figshare.27628344.v1
title: Enriched Tourism Dataset Paris (POIs)

description: Please, cite the following publication:----R. Hermoso, S. Ilarri, R. Trillo-Lado, & C. Marzo (2017). Recommending Needles in a Haystack: The SURGE Approach. <i>International Journal of Geographical Information Systems, 2025</i>.---This dataset contains the Paris subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/paris-attraction.csv). The original dataset comprises 4,351 entries that encompass a variety of attractions across Paris, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 477 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of Paris' attractions.- <b>Paris.csv</b>It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.- <b>Paris_annotated.csv</b>It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories.It has the following columns:* POI name* POI's address* One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.
DOI: 10.25397/eur.28314236.v1
title: Patient-relevance of outcome measures in breast cancer clinical trials - Data Files

description: The primary research question for which these data have been used, was: ‘How patient-relevant are outcomes measured in clinical trials for breast cancer drugs?’. Subquestions were: 1. Which treatment outcomes are relevant for breast cancer patients? 2. Which outcome measures are used in clinical trials for breast cancer drugs? 3. How much overlap is there between patient-relevant outcomes and outcomes measured in clinical trials?The dataset has been used to answer subquestion 2. Data have been obtained by searching Clinicaltrials.gov for trials conducted between January 2014 and March 2024 inclusive. Further inclusion criteria were that studies had to be phase III trials and had to focus on breast cancer, adults (18-64 years old) and drugs. Interventions focusing on lifestyle changes, Chinese medicine, anaesthesia, surgery and diagnostic methods were excluded. Ultimately, 264 trials were included and forty-five excluded. To determine the outcome measures used, the study plan of every included trial was reviewed and recorded on the data sheet.
DOI: 10.25397/eur.28706093
title: Loss aversion in EQ-5D-Y-3L: does it explain willingness to trade-off life duration in adult and child perspectives?

description: This project entailed the collection of (1) cTTO utilities for health states for a 10-year-old child, (2) cTTO utilities for corresponding health states from the perspective of the respondents themselves and (3) loss aversion coefficients for both the child and the adult perspective, among 100 UK adults. The aim of this study was to correct the cTTO utilities for loss aversion (i.e., losses of life duration weighing more than gains of the same size), to see whether this explained reluctance to trade-off life duration for a 10-year-old child.A read-me file and the R-code have been included in this folder. The full paper was published in the European Journal of Health Economics and can be found here: Loss aversion in EQ-5D-Y-3L: does it explain differences in willingness to trade-off life years in adults and children?
DOI: 10.25397/eur.28706093.v1
title: Loss aversion in EQ-5D-Y-3L: does it explain willingness to trade-off life duration in adult and child perspectives?

description: This project entailed the collection of (1) cTTO utilities for health states for a 10-year-old child, (2) cTTO utilities for corresponding health states from the perspective of the respondents themselves and (3) loss aversion coefficients for both the child and the adult perspective, among 100 UK adults. The aim of this study was to correct the cTTO utilities for loss aversion (i.e., losses of life duration weighing more than gains of the same size), to see whether this explained reluctance to trade-off life duration for a 10-year-old child.A read-me file and the R-code have been included in this folder. The full paper was published in the European Journal of Health Economics and can be found here: Loss aversion in EQ-5D-Y-3L: does it explain differences in willingness to trade-off life years in adults and children?
DOI: 10.15468/dl.dtmt3p
title: Occurrence Download

description: A dataset containing 380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "TaxonKey is Sibylla pretiosa"
 ]
}

 The dataset includes 380 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054932-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25397/eur.29086739
title: Research Data for Consumer Sentiment

description: This data set contains the data used in the research project "Cognitive Biases in Consumer Sentiment: the Peak-End Rule and Herding". The following files and items are includedICSdata.xlsx: Index of Consumer Sentiment and its constituents (sheet 1), and PAGO per region (sheet 2); original source University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/ALFRED_data: macro economic series related to economic growth, inflation, (un)employment and consumption, including publication date; original source ArchivaL Federal Reserve Economic Data (ALFRED), https://alfred.stlouisfed.org/; for each series a README sheet is included with metadataFREDdata: financial and economic series related to stock, bond, housing markets, interest rates,gasoline prices and regional unemployment rates; each sheet contains the mnemonic of the donwloaded series.MicroData_20220113: demographic information of each respondent in the Survey of Consumers conducted by the University of Michigan; downloaded from University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/Prelim_PA.xlsx: the Index of Consumer Sentiment and its constituent series, as reported in the preliminary annoucement by the University of Michigan (prelim), and the series constructed based on the surveys after the preliminary announcements. The prelim series are publicly available via https://data.sca.isr.umich.edu/ . The pa series have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.DemographicDifferences.xlsx: average differences between the prelim and pa monthly subsample in the demographic statistics available in MicroData_20220113.xlsx. The difference have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.<b>Methodology: </b>Linear regressions and time-series methods.<b>Findings</b>: We show that two heuristics, the peak-end rule and herding, generate biases in indexes of consumer sentiment. Both affect respondents' assessment of changes in their financial position over the past year. Conform the peak-end rule, their answers relate more to extreme detrimental monthly than to yearly changes in key financial and macro variables. These effects are stronger for more salient variables. As for herding, we document that respondents interviewed in the second round about past financial changes rely too strongly on future expectations from first-round respondents. These effects persist when we account for structural differences in sample composition or for the effect of other predictive variables. Our research shows the presence of both biases outside controlled environments and sheds new light on the relevance of sentiment indexes.
DOI: 10.25397/eur.29086739.v1
title: Research Data for Consumer Sentiment

description: This data set contains the data used in the research project "Cognitive Biases in Consumer Sentiment: the Peak-End Rule and Herding". The following files and items are includedICSdata.xlsx: Index of Consumer Sentiment and its constituents (sheet 1), and PAGO per region (sheet 2); original source University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/ALFRED_data: macro economic series related to economic growth, inflation, (un)employment and consumption, including publication date; original source ArchivaL Federal Reserve Economic Data (ALFRED), https://alfred.stlouisfed.org/; for each series a README sheet is included with metadataFREDdata: financial and economic series related to stock, bond, housing markets, interest rates,gasoline prices and regional unemployment rates; each sheet contains the mnemonic of the donwloaded series.MicroData_20220113: demographic information of each respondent in the Survey of Consumers conducted by the University of Michigan; downloaded from University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/Prelim_PA.xlsx: the Index of Consumer Sentiment and its constituent series, as reported in the preliminary annoucement by the University of Michigan (prelim), and the series constructed based on the surveys after the preliminary announcements. The prelim series are publicly available via https://data.sca.isr.umich.edu/ . The pa series have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.DemographicDifferences.xlsx: average differences between the prelim and pa monthly subsample in the demographic statistics available in MicroData_20220113.xlsx. The difference have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.<b>Methodology: </b>Linear regressions and time-series methods.<b>Findings</b>: We show that two heuristics, the peak-end rule and herding, generate biases in indexes of consumer sentiment. Both affect respondents' assessment of changes in their financial position over the past year. Conform the peak-end rule, their answers relate more to extreme detrimental monthly than to yearly changes in key financial and macro variables. These effects are stronger for more salient variables. As for herding, we document that respondents interviewed in the second round about past financial changes rely too strongly on future expectations from first-round respondents. These effects persist when we account for structural differences in sample composition or for the effect of other predictive variables. Our research shows the presence of both biases outside controlled environments and sheds new light on the relevance of sentiment indexes.
DOI: 10.25397/eur.29086739.v2
title: Research Data for Consumer Sentiment

description: This data set contains the data used in the research project "Cognitive Biases in Consumer Sentiment: the Peak-End Rule and Herding". The following files and items are includedICSdata.xlsx: Index of Consumer Sentiment and its constituents (sheet 1), and PAGO per region (sheet 2); original source University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/ALFRED_data: macro economic series related to economic growth, inflation, (un)employment and consumption, including publication date; original source ArchivaL Federal Reserve Economic Data (ALFRED), https://alfred.stlouisfed.org/; for each series a README sheet is included with metadataFREDdata: financial and economic series related to stock, bond, housing markets, interest rates,gasoline prices and regional unemployment rates; each sheet contains the mnemonic of the donwloaded series.MicroData_20220113: demographic information of each respondent in the Survey of Consumers conducted by the University of Michigan; downloaded from University of Michigan, Survey of Consumers, https://data.sca.isr.umich.edu/Prelim_PA.xlsx: the Index of Consumer Sentiment and its constituent series, as reported in the preliminary annoucement by the University of Michigan (prelim), and the series constructed based on the surveys after the preliminary announcements. The prelim series are publicly available via https://data.sca.isr.umich.edu/ . The pa series have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.DemographicDifferences.xlsx: average differences between the prelim and pa monthly subsample in the demographic statistics available in MicroData_20220113.xlsx. The difference have been constructed based on interview datas obtains from the University of Michigan. These data are proprietory and cannot be shared freely.<b>Methodology: </b>Linear regressions and time-series methods.<b>Findings</b>: We show that two heuristics, the peak-end rule and herding, generate biases in indexes of consumer sentiment. Both affect respondents' assessment of changes in their financial position over the past year. Conform the peak-end rule, their answers relate more to extreme detrimental monthly than to yearly changes in key financial and macro variables. These effects are stronger for more salient variables. As for herding, we document that respondents interviewed in the second round about past financial changes rely too strongly on future expectations from first-round respondents. These effects persist when we account for structural differences in sample composition or for the effect of other predictive variables. Our research shows the presence of both biases outside controlled environments and sheds new light on the relevance of sentiment indexes.
DOI: 10.34740/kaggle/dsv/13488717
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.dh5xdj
title: Occurrence Download

description: A dataset containing 2416 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria japonica",
 "HasCoordinate is true",
 "Country is Poland"
 ]
}

 The dataset includes 2416 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054934-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25397/eur.8072294
title: Replication archive Peeters (2018) Int. J. Forecasting

description: This item contains the replication files for Peeters (2018). Testing the Wisdom of Crowds in the Field: Transfermarkt Valuations and International Soccer Results. International Journal of Forecasting, 34 (1), 17-29. doi: 10.1016/j.ijforecast.2017.08.002<br>Please read the readme document first.
DOI: 10.25397/eur.8072294.v1
title: Replication data Peeters (2018) Int. J. Forecasting

description: This item contains the replication files for Peeters (2018). Testing the Wisdom of Crowds in the Field: Transfermarkt Valuations and International Soccer Results. International Journal of Forecasting, 34 (1), 17-29. doi: 10.1016/j.ijforecast.2017.08.002<br>Please read the readme document first.
DOI: 10.25397/eur.8072294.v2
title: Replication archive Peeters (2018) Int. J. Forecasting

description: This item contains the replication files for Peeters (2018). Testing the Wisdom of Crowds in the Field: Transfermarkt Valuations and International Soccer Results. International Journal of Forecasting, 34 (1), 17-29. doi: 10.1016/j.ijforecast.2017.08.002<br>Please read the readme document first.
DOI: 10.25397/eur.8152748
title: Historical Data: International monthly government bond returns

description: Academics and research analysts in financial economics frequently use returns on government bonds for their empirical analyses. In the United States, government bonds are also called Treasury bonds. The Federal Reserve publishes the yield-to-maturity of Treasury bonds. However, the Treasury bond returns earned by investors are not publicly available. The purpose of this study is to provide these currently not publicly available return series and provide formulas such that these series can easily be updated by researchers. We use standard textbook formulas to convert the yield-to-maturity data to investor returns. The starting date of our series is January 1962, when end-of-month data on the yield-to-maturity become publicly available. We compare our newly created total return series with alternative series that can be purchased. Our return series are very close, suggesting that they are a high-quality public alternative to commercially available data. <br> This data set calculates monthly returns for: United States (starting 1947) Germany (starting 1972) Japan (starting 1974) Australia (starting 1969)<br> France (starting 1987) Canada (starting 1986) UK (starting 1970) Norway (starting 1921) Sweden (starting 1920)<br> <br> Data updated unitl December 2022. <br>
DOI: 10.25397/eur.8152748.v1
title: Data: Treasury Bond Return Data Starting in 1962

description: Although the Federal Reserve publishes the yield-to-maturity of U.S. Treasury bonds, the returns earned by investors are not publicly available. We estimate a return series starting in 1962 using yield-to-maturity data of government bonds with 10 year maturity and include formulas to update the paper going forward.
DOI: 10.25397/eur.8152748.v2
title: Data: Treasury Bond Return Data Starting in 1962

description: Although the Federal Reserve publishes the yield-to-maturity of U.S. Treasury bonds, the returns earned by investors are not publicly available. We estimate a return series starting in 1962 using yield-to-maturity data of government bonds with 10 year maturity and include formulas to update the paper going forward.
DOI: 10.25397/eur.8152748.v3
title: Data: Treasury Bond Return Data Starting in 1962

description: Although the Federal Reserve publishes the yield-to-maturity of U.S. Treasury bonds, the returns earned by investors are not publicly available. We estimate a return series starting in 1962 using yield-to-maturity data of government bonds with 10 year maturity and include formulas to update the paper going forward.