cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e188121
title: Data and Code for: M&A and Innovation: A New Classification of Patents

description: Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining M&A data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.
DOI: 10.3886/e188121v1
title: Data and Code for: M&A and Innovation: A New Classification of Patents

description: Policymakers are increasingly concerned that incumbent acquisitions of small or young firms may slow down rather than speed up innovation, but it is difficult to identify which firms are related in the fast-changing space of technological innovation. This paper proposes a new, data-driven method to classify patent data into tech-business zones on a probabilistic basis, using patent assignee information. After combining M&A data from S&P Global Market Intelligence with PatentsView data from the US Patent and Trademark Office, we discuss how the zone classification can aid merger reviews and other lines of research.
DOI: 10.3886/e188161v1
title: Data and Code for: News Media, Inflation, and Sentiment

description: Data and Code accompanying the paper: We study the relationship between media portrayals of inflation and consumer sentiment. Using tools from natural language processing, we uncover two competing topics in US news coverage of inflation: the first relates inflation to financial variables, while the second relates inflation to real variables. As inflation rose in 2021, media increasingly emphasized the real economy. Linking inflation news to social-network data from Twitter, we find that exposure to articles emphasizing the connection between inflation and the real economy significantly reduces sentiment, particularly in periods of high inflation. Shifting media narratives may therefore have contributed to declining consumer sentiment in 2021.<br>
description: prevalence of inflation narratives;popularity of newspapers on Twitter;effects of inflation narratives in the media on sentiment<br>.
DOI: 10.6073/pasta/c1aeda1474696506ade16547f09a4c52
title: Imnavait Watershed Thaw Depth Survey Summary for 2003 to 2022, Arctic LTER, Toolik Research Station, Alaska.

description: Thaw depth was measured using a steel probe in the Imnavait Creek watershed, near Toolik Lake, Alaska. The thaw grid includes measurements made from the valley bottom (on both sides of the stream), up the hillslope. The thaw grid is near Imnavait water tracks 7 and 8, and measurements have been made from the 2003 season until present. At least two surveys are conducted each summer, on 2 July and on 11 August (plus or minus a couple of days on either side of those dates).
DOI: 10.3886/e185901
title: Data and Code for: Labor Mobility and Unemployment over the Business Cycle

description: We estimate the responsiveness of net labor migration to regional differences in unemployment rates across the United States since the mid-1970s. Our baseline estimate suggests an elasticity of roughly -0.3. For typical labor force participation ratios, an increase of 100 unemployed workers in an area is associated with net out-migration of roughly 47 workers. Instrumenting for regional unemployment produces even higher estimates. Our estimates are stable over time, inclusive of the Great Recession. The estimates depend crucially on accurate data and accounting for long-term trends in migration and unemployment.<br>
DOI: 10.3886/e185901v1
title: Data and Code for: Labor Mobility and Unemployment over the Business Cycle

description: We estimate the responsiveness of net labor migration to regional differences in unemployment rates across the United States since the mid-1970s. Our baseline estimate suggests an elasticity of roughly -0.3. For typical labor force participation ratios, an increase of 100 unemployed workers in an area is associated with net out-migration of roughly 47 workers. Instrumenting for regional unemployment produces even higher estimates. Our estimates are stable over time, inclusive of the Great Recession. The estimates depend crucially on accurate data and accounting for long-term trends in migration and unemployment.<br>
DOI: 10.25345/c5cj87w4z
title: MassIVE MSV000091819 - Deep proteomics profiling of quadriceps and heart muscle in a murine model of orthotopic pancreatic cancer treated with gemcitabine plus nab-paclitaxel
title: Twelve-week-old male C57Bl/6J mice were orthotopically injected with KPC cells into the pancreas or underwent a sham surgery. On day 4 and 10 post tumor cell injection, mice received IP injections of 120mg/kg of gemcitabine and 10mg/kg of nab-paclitaxel (GnP) or saline as vehicle control. The groups were sham + vehicle (n=4), KPC + vehicle (n=4) and KPC + GnP (n=4). Mice were euthanized on day 14 post tumor cell injection. Quadriceps and heart muscles were snap frozen. Following protein extraction from frozen tissues, extracts were alkylated and underwent proteolytic digestion. Next, tandem mass tag isobaric labeling was used followed by spectral data for each peptide by Proteome DiscovereTM2.4. Mus Musculus proteins were downloaded from the Universal Protein Knowledgebase to identify proteins. False discovery rate filtration of 1% was applied to the peptide-spectrum match and protein levels.
DOI: 10.5281/zenodo.7851380
title: Pilot 2 - Research Data

description: In Pilot 2, data have been collected mainly for the following purposes: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE CLB app. In Pilot 2, anonymized student data have been collected in correspondence with the research objective of testing the efficiency of Augmented Reality for STEM education, to evaluate if the CLB apps can help students improve test scores by up to 33 % and increase the retention rate by up to 100 %. Additionally, pedagogical aspects of the AR use and its impact on teaching and learning will be evaluated. To respond to these claims, the methodology applied included pre-tests and post-tests for students and additional qualitative surveys for teachers. Pilot 2 collected only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period. As in the case of Pilot 1, and in accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 2. The only personal information collected from students and their parents/legal guardians will be for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 2 in order to ensure that: the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.
DOI: 10.5281/zenodo.7851379
title: Pilot 2 - Research Data

description: In Pilot 2, data have been collected mainly for the following purposes: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE CLB app. In Pilot 2, anonymized student data have been collected in correspondence with the research objective of testing the efficiency of Augmented Reality for STEM education, to evaluate if the CLB apps can help students improve test scores by up to 33 % and increase the retention rate by up to 100 %. Additionally, pedagogical aspects of the AR use and its impact on teaching and learning will be evaluated. To respond to these claims, the methodology applied included pre-tests and post-tests for students and additional qualitative surveys for teachers. Pilot 2 collected only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period. As in the case of Pilot 1, and in accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 2. The only personal information collected from students and their parents/legal guardians will be for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 2 in order to ensure that: the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.
DOI: 10.5281/zenodo.7872837
title: Pilot 2 - Research Data

description: In Pilot 2, data have been collected mainly for the following purposes: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE CLB app. In Pilot 2, anonymized student data have been collected in correspondence with the research objective of testing the efficiency of Augmented Reality for STEM education, to evaluate if the CLB apps can help students improve test scores by up to 33 % and increase the retention rate by up to 100 %. Additionally, pedagogical aspects of the AR use and its impact on teaching and learning will be evaluated. To respond to these claims, the methodology applied included pre-tests and post-tests for students and additional qualitative surveys for teachers. Pilot 2 collected only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period. As in the case of Pilot 1, and in accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 2. The only personal information collected from students and their parents/legal guardians will be for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 2 in order to ensure that: the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.
DOI: 10.5281/zenodo.7876954
title: Pilot 2 - Research Data

description: In Pilot 2, data have been collected mainly for the following purposes: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE CLB app. In Pilot 2, anonymized student data have been collected in correspondence with the research objective of testing the efficiency of Augmented Reality for STEM education, to evaluate if the CLB apps can help students improve test scores by up to 33 % and increase the retention rate by up to 100 %. Additionally, pedagogical aspects of the AR use and its impact on teaching and learning will be evaluated. To respond to these claims, the methodology applied included pre-tests and post-tests for students and additional qualitative surveys for teachers. Pilot 2 collected only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period. As in the case of Pilot 1, and in accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 2. The only personal information collected from students and their parents/legal guardians will be for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 2 in order to ensure that: the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.
DOI: 10.3886/e187384
title: Data and Code for: Selection Bias in Voluntary Random Testing: Evidence from a COVID- 19 Antibody Study

description: We use data from a serological study that experimentally varied financial incentives for participation to detect and characterize selection bias. Participants are from neighborhoods with substantially lower COVID-19 risks. Existing methods to account for the resulting selection bias produce wide bounds or estimates that are inconsistent with the population. One explanation for these inconsistent estimates is that the underlying methods presume a single dimension of unobserved heterogeneity. The data suggests there are two types of nonparticipants with opposing selection patterns. Allowing for these different types may lead to better accounting for selection bias.
DOI: 10.3886/e187384v1
title: Data and Code for: Selection Bias in Voluntary Random Testing: Evidence from a COVID- 19 Antibody Study

description: We use data from a serological study that experimentally varied financial incentives for participation to detect and characterize selection bias. Participants are from neighborhoods with substantially lower COVID-19 risks. Existing methods to account for the resulting selection bias produce wide bounds or estimates that are inconsistent with the population. One explanation for these inconsistent estimates is that the underlying methods presume a single dimension of unobserved heterogeneity. The data suggests there are two types of nonparticipants with opposing selection patterns. Allowing for these different types may lead to better accounting for selection bias.
DOI: 10.6084/m9.figshare.22720378
title: Percent cover of intertidal mussels on the Atlantic coast of Nova Scotia, Canada.

description: This file provides data on the percent cover of intertidal mussels (<em>Mytilus</em>) at the high-to-mid intertidal zone of wave-exposed bedrock locations along the open Atlantic coast of mainland Nova Scotia, Canada. Each value represents mussel percent cover measured using a 20-cm-x-20-cm quadrat.
DOI: 10.5281/zenodo.7874633
title: TCHES artefact dataset for paper : Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations

description: This dataset allows reproducing the results of the CHES 2023 paper : "Efficient Regression-Based Linear Discriminant<br> Analysis for Side-Channel Security Evaluations". It contains the side-channel measurements necessary to do so. Scripts and readme is available at the CHES artefact site : &lt;link not yet alive&gt;
DOI: 10.5281/zenodo.7874632
title: TCHES artefact dataset for paper : Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations

description: This dataset allows reproducing the results of the CHES 2023 paper : "Efficient Regression-Based Linear Discriminant<br> Analysis for Side-Channel Security Evaluations". It contains the side-channel measurements necessary to do so. Scripts and readme is available at the CHES artefact site : &lt;link not yet alive&gt;
DOI: 10.3886/e186169v1
title: Data and Code for: “Do Government Spending Multipliers Depend on the Sign of the Shock?”

description: We analyze whether government spending multipliers differ by the sign of the shock. Using aggregate historical U.S. data, we apply Ben Zeev’s (2020) nonlinear diagnostic tests and find evidence of nonlinearities in the impulse response functions of both government spending and GDP. We then extend Ramey and Zubairy’s (2018) framework to allow for asymmetric effects as a type of state dependence to estimate multipliers. While we find differences in the impulse response functions, the resulting multipliers do not differ by sign of the shock. Thus, we find no evidence of asymmetry of government spending multipliers.
DOI: 10.3886/e185942
title: Data and Code for: "The Trickling Up of Excess Savings"

description: Code for "The Trickling Up of Excess Savings", AEA: Papers and Proceedings, 2023<br>
DOI: 10.3886/e185942v1
title: Data and Code for: "The Trickling Up of Excess Savings"

description: Code for "The Trickling Up of Excess Savings", AEA: Papers and Proceedings, 2023<br>
DOI: 10.3886/e185601
title: Data and Code for: The Insurance Implications of Government Student Loan Repayment Schemes

description: We use new administrative data that links detailed information on Canadian student loan recipients with their repayment and income histories from the Canada Student Loans Program (CSLP), income tax filings, and post-secondary schooling records to measure the extent to which student borrowers adjust loan repayments to insure against income variation. Several mechanisms are available for students to adjust loan repayments in response to income fluctuations: formal, like CSLP's Repayment Assistance Plan; and informal, such as delinquency or default. Borrowers can also make larger payments than required should they experience unexpectedly high income. Indeed, loan payments are shown to increase in income, more so in early years and for individuals with higher initial debt. More formally, we estimate that on average, an unexpected $1,000 change in year-over-year income is associated with a $30 change in loan payment: from a $50 change the year after graduation, declining to a $20 change 5 years after graduation. Loan repayments are also used to absorb income variation that is more permanent in nature: for borrowers whose income is consistently below or above expected income at graduation, the magnitude of average repayment adjustment is similar to the average yearly response.
DOI: 10.3886/e185601v1
title: Data and Code for: The Insurance Implications of Government Student Loan Repayment Schemes

description: We use new administrative data that links detailed information on Canadian student loan recipients with their repayment and income histories from the Canada Student Loans Program (CSLP), income tax filings, and post-secondary schooling records to measure the extent to which student borrowers adjust loan repayments to insure against income variation. Several mechanisms are available for students to adjust loan repayments in response to income fluctuations: formal, like CSLP's Repayment Assistance Plan; and informal, such as delinquency or default. Borrowers can also make larger payments than required should they experience unexpectedly high income. Indeed, loan payments are shown to increase in income, more so in early years and for individuals with higher initial debt. More formally, we estimate that on average, an unexpected $1,000 change in year-over-year income is associated with a $30 change in loan payment: from a $50 change the year after graduation, declining to a $20 change 5 years after graduation. Loan repayments are also used to absorb income variation that is more permanent in nature: for borrowers whose income is consistently below or above expected income at graduation, the magnitude of average repayment adjustment is similar to the average yearly response.