cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3761562.v1688
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25919/6wd6-zv86
title: Parkes observations for project P1067 semester 2020APRS_16

description: The Parkes Ultra-wide-bandwidth receiver enables, for the first time with the Parkes telescope, a commensal high time and high frequency resolution survey. We will observe Baade's window in which we are guaranteed detections of pulsars and spectral lines. Our science drivers are diverse and range from understanding scattering, to detecting new pulsars, to searching for galaxies in the zone of avoidance, probing the globular cluster at the centre of the window and studying OH circumstellar masers and CO-dark molecular gas. The observing strategy and processing software developed for this survey will be applicable to all future Parkes surveys with the wide-band receiver or cyrogenically-cooled phased array feed.
DOI: 10.25919/kmdf-0v80
title: Parkes observations for project P1032 semester 2020APRS_14

description: We propose timing observations of southern double neutron star (DNS) systems to obtain precise estimates of the component masses and thus increase the statistics of neutron star mass distribution. The improved sensitivity of the Ultra Wideband Low (UWL) receiver will provide a 3X increase in the precision of the time of arrivals (ToAs) of the pulses compared to the multibeam data. This has the potential to perform quick measurements of masses for a number of binary systems in the next 1-2 years. We will also use MeerKAT observations at crucial orbital phases to increase our chances of mass measurements. Increase in the number of measured NS masses has significant implications for understanding NS internal structure and for stellar and binary evolution physics.
DOI: 10.5281/zenodo.4287680
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Stylianos Chatzimanolis, https://orcid.org/0000-0001-9008-0611. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.17632/bn2pskgxp3.1
title: Data for: Evaluation of the Effectiveness of a Multi-Sectional Façade with Venetian Blinds and Roller Shades with Automated Shading Control Strategies

description: Weather data in Taipei and New York City in .epw format and some .xml material files of complex fenestration systems.
DOI: 10.5284/1083486
title: Bletchingdon Park House, Bletchingdon, Oxfordshire. Archaeological Evaluation. (OASIS ID: johnmoor1-363553)

description: John Moore Heritage Services carried out 42 trench evaluation at the Bletchingdon Park estate in Bletchingdon, Oxfordshire. Three boundary ditches were found in the northern fields; all the remaining archaeological evidence came from the southern approach to the main house, all containing material predominantly medieval in date. Features of note were a number of substantial ditches and pits, a potential structural foundation wall, earlier landscaping evidence (including construction methods as well as dividing walls and ditches) and the earlier garden boundary wall and road leading to St Giles Church from Bletchingdon village.
DOI: 10.15468/zqdzcd
title: Taxonomic revision of the Australian Notoxinae (Coleoptera: Anthicidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kejval, Zbyněk, Cz, Domažlice (2013): Taxonomic revision of the Australian Notoxinae (Coleoptera: Anthicidae). Acta Entomologica Musei Nationalis Pragae (suppl.) 53: 1-98, DOI: http://doi.org/10.5281/zenodo.4272709
DOI: 10.25345/c5cn3m
title: MassIVE MSV000086512 - GNPS_Synthetic Cannabinoids_DDA25_0.7
title: Assessment metabolism synthtetic cannabinoids DDA 25% untargeted approach
DOI: 10.5281/zenodo.4287704
title: ICESat-2 monthly gridded winter Arctic sea ice thickness

description: Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below. Temporal range: November 2018 - April 2019, October 2019 to April 2020. Data: A single netCDF file is included for each month. Variables include: Sea ice freeboard (from ATL10) Snow depth (redistributed NESOSIM) Snow density (redistributed NESOSIM) Bulk sea ice density Sea ice type (from OSI SAF) Sea ice thickness uncertainty Mean day of month in a given grid cell Number of freeboard segments in a given grid cell. A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication. Key changes from version 1 (Petty et al., 2020) to version 2 include: Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020). Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (https://github.com/akpetty/NESOSIM). Use of all three strong beams (instead of just strong beam #1). The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em>
description: The original (version 1) sea ice thickness data are available at https://doi.org/10.5067/CV6JEXEE31HF
description: {"references": ["Petty, A. A., N. T. Kurtz, R. Kwok, T. Markus, T. A. Neumann (2020), Winter Arctic sea ice thickness from ICESat\u20102 freeboards, Journal of Geophysical Research: Oceans, 125, e2019JC015764. doi:10.1029/2019JC015764", "Kwok, R., A. A. Petty, M. Bagnardi, N. T. Kurtz, G. F. Cunningham, A. Ivanoff (2020), Refining the sea surface identification approach for determining freeboard in the ICESat-2 sea ice products. The Cryosphere Discuss, doi:10.5194/tc-2020-174."]}
DOI: 10.5281/zenodo.4287703
title: ICESat-2 monthly gridded winter Arctic sea ice thickness

description: Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below. Temporal range: November 2018 - April 2019, October 2019 to April 2020. Data: A single netCDF file is included for each month. Variables include: Sea ice freeboard (from ATL10) Snow depth (redistributed NESOSIM) Snow density (redistributed NESOSIM) Bulk sea ice density Sea ice type (from OSI SAF) Sea ice thickness uncertainty Mean day of month in a given grid cell Number of freeboard segments in a given grid cell. A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication. Key changes from version 1 (Petty et al., 2020) to version 2 include: Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020). Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (https://github.com/akpetty/NESOSIM). Use of all three strong beams (instead of just strong beam #1). The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em>
description: The original (version 1) sea ice thickness data are available at https://doi.org/10.5067/CV6JEXEE31HF
description: {"references": ["Petty, A. A., N. T. Kurtz, R. Kwok, T. Markus, T. A. Neumann (2020), Winter Arctic sea ice thickness from ICESat\u20102 freeboards, Journal of Geophysical Research: Oceans, 125, e2019JC015764. doi:10.1029/2019JC015764", "Kwok, R., A. A. Petty, M. Bagnardi, N. T. Kurtz, G. F. Cunningham, A. Ivanoff (2020), Refining the sea surface identification approach for determining freeboard in the ICESat-2 sea ice products. The Cryosphere Discuss, doi:10.5194/tc-2020-174."]}
DOI: 10.3886/e120232v1-70560
title: Code for: How well targeted are soda taxes?

description: Soda taxes aim to reduce excessive sugar consumption. We assess who are most impacted by soda taxes. We estimate demand using micro longitudinal data covering on-the-go purchases, and exploit the panel dimension to estimate individual specific preferences. We relate these preferences and counterfactual predictions to individual characteristics and show that soda taxes are relatively effective at targeting the sugar intake of the young, are less successful at targeting the intake of those with high total dietary sugar, and are unlikely to be strongly regressive especially if consumers benefit from averted internalities.
DOI: 10.3886/e127241v1
title: Amazon-Google, Augmented Version, Fixed Splits

description: Motivation:<br>Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits. <br><br>Dataset Description:<br>An augmented version of the amazon-google products dataset for benchmarking entity matching/record linkage methods found at: <br>https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolutio...<br>The augmented version adds a fixed set of non-matching pairs to the original dataset. In addition, fixed splits for training, validation and testing as well as their corresponding feature vectors are provided. The feature vectors are built using data type specific similarity metrics.<br>The dataset contains 1,363 records describing products deriving from amazon which are matched against 3,226 product records from google. The gold standards have manual annotations for 1,298 matching and 6,306 non-matching pairs. The total number of attributes used to decribe the product records are 4 while the attribute density is 0.75.<br>The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.<br>The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:<br>http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html<br>
DOI: 10.3886/e127242
title: Restaurants (Fodors-Zagats), Augmented Version, Fixed Splits

description: Motivation:<br> Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits. <br><br> Dataset Description:<br> An augmented version of the fodors-zagats restaurants dataset for benchmarking entity matching/record linkage methods found at:<br>https://hpi.de/en/naumann/projects/data-integration-data-quality-and-data-cleansing/dude.html#c11471<br><br> The augmented version adds a fixed set of non-matching pairs to the original dataset. In addition, fixed splits for training, validation and testing as well as their corresponding feature vectors are provided. The feature vectors are built using data type specific similarity metrics.<br><br>The dataset contains 533 records describing restaurants from fodors.com which are matched against 331 restaurants records from zagat.com. The gold standards have manual annotations for 112 matching and 488 non-matching pairs. The total number of attributes used to decribe the product records are 5 while the attribute density is 100%.<br><br>The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.<br> The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:<br> http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html
DOI: 10.3886/e127242v1
title: Restaurants (Fodors-Zagats), Augmented Version, Fixed Splits

description: Motivation:<br> Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits. <br><br> Dataset Description:<br> An augmented version of the fodors-zagats restaurants dataset for benchmarking entity matching/record linkage methods found at:<br>https://hpi.de/en/naumann/projects/data-integration-data-quality-and-data-cleansing/dude.html#c11471<br><br> The augmented version adds a fixed set of non-matching pairs to the original dataset. In addition, fixed splits for training, validation and testing as well as their corresponding feature vectors are provided. The feature vectors are built using data type specific similarity metrics.<br><br>The dataset contains 533 records describing restaurants from fodors.com which are matched against 331 restaurants records from zagat.com. The gold standards have manual annotations for 112 matching and 488 non-matching pairs. The total number of attributes used to decribe the product records are 5 while the attribute density is 100%.<br><br>The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.<br> The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:<br> http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html
DOI: 10.3886/e127243
title: Web Data Commons Phones Dataset, Augmented Version, Fixed Splits

description: Motivation:<br> Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits. <br><br>Dataset Description:<br> An augmented version of the wdc phones dataset for benchmarking entity matching/record linkage methods found at:<br>http://webdatacommons.org/productcorpus/index.html#toc4<br> <br>The augmented version adds fixed splits for training, validation and testing as well as their corresponding feature vectors. The feature vectors are built using data type specific similarity metrics.<br>The dataset contains 447 records describing products deriving from 17 e-shops which are matched against a product catalog of 50 products. The gold standards have manual annotations for 258 matching and 22,092 non-matching pairs. The total number of attributes used to decribe the product records are 26 while the attribute density is 0.25.<br> <br>The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.<br> The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:<br> http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html<br>
DOI: 10.3886/e127243v1
title: Web Data Commons Phones Dataset, Augmented Version, Fixed Splits

description: Motivation:<br> Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits. <br><br>Dataset Description:<br> An augmented version of the wdc phones dataset for benchmarking entity matching/record linkage methods found at:<br>http://webdatacommons.org/productcorpus/index.html#toc4<br> <br>The augmented version adds fixed splits for training, validation and testing as well as their corresponding feature vectors. The feature vectors are built using data type specific similarity metrics.<br>The dataset contains 447 records describing products deriving from 17 e-shops which are matched against a product catalog of 50 products. The gold standards have manual annotations for 258 matching and 22,092 non-matching pairs. The total number of attributes used to decribe the product records are 26 while the attribute density is 0.25.<br> <br>The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.<br> The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:<br> http://data.dws.informatik.uni-mannheim.de/benchmarkmatchingtasks/index.html<br>
DOI: 10.3886/e127081v1-70567
title: COVID Isolation on Sleep and Health in Healthcare Workers

description: Brief Summary Statement Current Knowledge/Study Rationale: Previous studies conducted across the globe have shown higher stress, worsening mental health, and worsening sleep patterns in front line healthcare workers during the COVID-19 pandemic. The rationale for this study was to examine whether sleep, mood, and health related behaviors might differ between healthcare workers who transitioned to conducting care from home and those who continued to report in-person to their respective hospitals or healthcare facilities. Study Impact: This study shows worsening mood across healthcare workers, regardless of whether they were working from home or continuing to report to work. Our findings highlight the need for support programs to monitor mood and health during the during the COVID-19 pandemic.
DOI: 10.3886/e119743v1
title: Data and Code for: Competition and Entry in Agricultural Markets: Experimental Evidence from Kenya

description: African agricultural markets are characterized by low farmer revenues and high consumer food prices. Many have worried that this wedge is partially driven by imperfect competition among intermediaries. This paper provides experimental evidence from Kenya on intermediary market structure. Randomized cost shocks and demand subsidies are used to identify a structural model of market competition. Estimates reveal that traders act consistently with joint profit maximization and earn median markups of 39%. Exogenously-induced firm entry has negligible effects on prices, and low take- up of subsidized entry offers implies large fixed costs. We estimate that traders capture 82% of total surplus.
description: Maize markets in Western Kenya.
DOI: 10.3886/e119387
title: Data and Code for: Discounts and Deadlines in Consumer Search

description: We present a new equilibrium search model where consumers initially search among discount opportunities, but are willing to pay more as a deadline approaches, eventually turning to full-price sellers. The model predicts equilibrium price dispersion and rationalizes discount and full-price sellers coexisting without relying on ex-ante heterogeneity. We apply the model to online retail sales via auctions and posted prices, where failed attempts to purchase reveal consumers' reservation prices. We find robust evidence supporting the theory. We quantify dynamic search frictions arising from deadlines and show how, with deadline-constrained buyers, seemingly neutral platform fee increases can cause large market shifts.<br><br>Data and code are available on the authors' websites and the AER data repository. Some of the data is proprietary and can only be accessed through following the instructions provided in the included README file.
DOI: 10.3886/e119387v2
title: Data and Code for: Discounts and Deadlines in Consumer Search

description: We present a new equilibrium search model where consumers initially search among discount opportunities, but are willing to pay more as a deadline approaches, eventually turning to full-price sellers. The model predicts equilibrium price dispersion and rationalizes discount and full-price sellers coexisting without relying on ex-ante heterogeneity. We apply the model to online retail sales via auctions and posted prices, where failed attempts to purchase reveal consumers' reservation prices. We find robust evidence supporting the theory. We quantify dynamic search frictions arising from deadlines and show how, with deadline-constrained buyers, seemingly neutral platform fee increases can cause large market shifts.<br><br>Data and code are available on the authors' websites and the AER data repository. Some of the data is proprietary and can only be accessed through following the instructions provided in the included README file.