cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc26hgck
title: CCDC 2040648: Experimental Crystal Structure Determination

description: Related Article: Daniel Eppel, Alpay A. Eryiğit, Matthias Rudolph, Margit Brückner, Frank Rominger, Abdullah M. Asiri, A. Stephen K. Hashmi|2021|Angew.Chem.,Int.Ed.|60|13636|doi:10.1002/anie.202017065
DOI: 10.5517/ccdc.csd.cc26nbw3
title: CCDC 2045345: Experimental Crystal Structure Determination

description: Related Article: Bin-Yuan Hu, Yun-Li Zhao, Deng-Sen Xiong, Ying-Jie He, Zhong-Shun Zhou, Pei-Feng Zhu, Zhao-Jie Wang, Yong-Liang Wang, Li-Xing Zhao, Xiao-Dong Luo|2021|Org.Lett.|23|4158|doi:10.1021/acs.orglett.1c01102
DOI: 10.5517/ccdc.csd.cc26nbx4
title: CCDC 2045346: Experimental Crystal Structure Determination

description: Related Article: Bin-Yuan Hu, Yun-Li Zhao, Deng-Sen Xiong, Ying-Jie He, Zhong-Shun Zhou, Pei-Feng Zhu, Zhao-Jie Wang, Yong-Liang Wang, Li-Xing Zhao, Xiao-Dong Luo|2021|Org.Lett.|23|4158|doi:10.1021/acs.orglett.1c01102
DOI: 10.5517/ccdc.csd.cc26xk5w
title: CCDC 2053228: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xk6x
title: CCDC 2053229: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xk7y
title: CCDC 2053230: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xk8z
title: CCDC 2053231: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xk90
title: CCDC 2053232: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkb1
title: CCDC 2053233: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkc2
title: CCDC 2053234: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkd3
title: CCDC 2053235: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkf4
title: CCDC 2053236: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkg5
title: CCDC 2053237: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkh6
title: CCDC 2053238: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkj7
title: CCDC 2053239: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkk8
title: CCDC 2053240: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26xkl9
title: CCDC 2053241: Experimental Crystal Structure Determination

description: Related Article: Chencheng Fu, Steven P. Kelley, Jianzhuo Tu, Michael Harmata|2021|J.Org.Chem.|86|7028|doi:10.1021/acs.joc.1c00032
DOI: 10.5517/ccdc.csd.cc26yrpm
title: CCDC 2054391: Experimental Crystal Structure Determination

description: Related Article: Toshinobu Nakajo, Jun Kumagai, Shinpei Kusaka, Akihiro Hori, Yuh Hijikata, Jenny Pirillo, Yunsheng Ma, Ryotaro Matsuda|2021|J.Am.Chem.Soc.|143|8129|doi:10.1021/jacs.1c02430
DOI: 10.5517/ccdc.csd.cc27h4nk
title: CCDC 2070138: Experimental Crystal Structure Determination

description: Related Article: Yinsong Wu, Xinwei He, Mengqing Xie, Ruxue Li, Yi Ning, Jiahui Duan, Enshen Zhang, Yongjia Shang|2021|J.Org.Chem.|86|7370|doi:10.1021/acs.joc.1c00259
DOI: 10.5517/ccdc.csd.cc27pwf8
title: CCDC 2076610: Experimental Crystal Structure Determination

description: Related Article: Leiqing Fu, Yunyun Liu, Jie-Ping Wan|2021|Org.Lett.|23|4363|doi:10.1021/acs.orglett.1c01301
DOI: 10.5517/ccdc.csd.cc27qysq
title: CCDC 2077644: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.5517/ccdc.csd.cc27qytr
title: CCDC 2077645: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 7.04–7.02 (m, 2H), 6.83 (d, J = 7.9 Hz, 1H), 5.14 (s, 1H), 3.91 (s, 3H), 3.87 (s, 3H), 3.11–3.03 (m, 2H), 2.94–2.89 (m, 2H), 2.21–2.15 (m, 1H), 1.99–1.88 (m, 1H). Impurities: water at 1.56 ppm and dichloromethane at 5.30 ppm.
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 149.1 (CCOCH3), 149.1 (CCOCH3), 131.7 (Cq), 120.1 (CH), 111.2 (CH), 110.8 (CH), 55.9 (CH3, 2C), 51.3 (CH), 32.3 (CH2, 2C), 25.1 (CH2).
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000480
title: electron ionisation mass spectrometry (EI-MS) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for electron ionisation mass spectrometry (EI-MS) MS (EI, 70 eV, 60 °C), m/z (%):257 (11) [M+H]+, 256 (69) [M]+, 183 (12), 182 (100), 181 (17), 177 (12), 151 (12), 58 (11). HRMS–EI (m/z): (C12H16O2S2) [M]+ calcd for 256.0586; found 256,0587.
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3010 (w), 3004 (w), 2952 (w), 2931 (w), 2901 (w), 2833 (w), 1602 (w), 1588 (w), 1511 (vs), 1473 (w), 1460 (s), 1439 (m), 1424 (m), 1414 (s), 1333 (w), 1269 (s), 1237 (vs), 1220 (vs), 1181 (s), 1156 (s), 1136 (vs), 1037 (m), 1021 (vs), 948 (w), 905 (w), 873 (m), 863 (s), 809 (s), 762 (vs), 748 (s), 727 (m), 704 (s), 679 (m), 653 (w), 611 (w), 594 (w), 578 (m), 469 (w), 458 (w), 397 (w), 387 (w) cm–1.
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0001076
title: carbon, hydrogen and nitrogen elemental analysis (CHN analysis) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for carbon, hydrogen and nitrogen elemental analysis (CHN analysis) EA (C12H16O2S2): Calcd C 56.22; H 6.29; S 25.01. Found C56.27 ; H 6.58; S 24.92.
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n.1
title: 2-(3,4-dimethoxyphenyl)-1,3-dithiane
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: This dataset contains experimental data from a sample (2-(3,4-dimethoxyphenyl)-1,3-dithiane, InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT) electron ionisation mass spectrometry (EI-MS) infrared absorption spectroscopy (IR) carbon, hydrogen and nitrogen elemental analysis (CHN analysis)
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 120.1 (+), 111.2 (+), 110.8 (+), 55.9 (+), 51.3 (+), 32.3 (–), 25.1 (–).
DOI: 10.14272/bysffoiedqtzbd-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) (2-(3,4-dimethoxyphenyl)-1,3-dithiane)
title: InChI=1S/C12H16O2S2/c1-13-10-5-4-9(8-11(10)14-2)12-15-6-3-7-16-12/h4-5,8,12H,3,6-7H2,1-2H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 120.1, 111.2, 110.8, 51.3.
DOI: 10.6084/m9.figshare.14736480
title: 210602_TABLES.docx

description: disability accommodation data
DOI: 10.5281/zenodo.4890048
title: BraCID: Brazilian Cultural Identity Information Through Reading Preferences

description: In Brazil, each region has its own cultural identity regarding accent, gastronomy, traditions, all of which may reflect its literature. Specially, we believe that country's background and contextual features are directly related to what people read. Hence, we present an enhanced dataset that comprises cultural, geographic, and socioeconomic information to explore Brazilian cultural identity through reading preference. As our main data source, we chose the Goodreads website due to the sheer volume of data available and its organized and easily accessible API. We collect data from Brazilian readers through the <em>goodreads </em>library, which provides a Python interface to the Goodreads API. Specifically, we collect members of two of the largest Brazilian reading groups: the "Clube de Leitores em Português" (4,229 members) and the "Goodreads Brasil" (3,222 members). For all members of both groups, we also collect data from their friends. Then, we filter only those containing Brazil as location information from the final users' set. Finally, with the same library, we gather users' bookshelves to assess their reading preferences. To investigate the Brazilian reading identity, we consider a medley of demographic and socioeconomic data from the Brazilian Institute of Geography and Statistics (IBGE): including territorial area, population estimate, demographic density, Human Development Index (HDI), Gross Domestic Product (GDP), and monthly household income per capita. All indicators refer to the year 2020, except the HDI that refers to the year 2017. The data collection was carried out from February 23 to March 04, 2021. Our final dataset, named as BraCID, comprises: <strong>38,231</strong> Brazilian Goodreads users <strong>75,093</strong> Distinct books <strong>80</strong> Literary genres <strong>6</strong> IBGE indicators regarding the <strong>27</strong> federative units of Brazil
DOI: 10.5281/zenodo.4890047
title: BraCID: Brazilian Cultural Identity Information Through Reading Preferences

description: In Brazil, each region has its own cultural identity regarding accent, gastronomy, traditions, all of which may reflect its literature. Specially, we believe that country's background and contextual features are directly related to what people read. Hence, we present an enhanced dataset that comprises cultural, geographic, and socioeconomic information to explore Brazilian cultural identity through reading preference. As our main data source, we chose the Goodreads website due to the sheer volume of data available and its organized and easily accessible API. We collect data from Brazilian readers through the <em>goodreads </em>library, which provides a Python interface to the Goodreads API. Specifically, we collect members of two of the largest Brazilian reading groups: the "Clube de Leitores em Português" (4,229 members) and the "Goodreads Brasil" (3,222 members). For all members of both groups, we also collect data from their friends. Then, we filter only those containing Brazil as location information from the final users' set. Finally, with the same library, we gather users' bookshelves to assess their reading preferences. To investigate the Brazilian reading identity, we consider a medley of demographic and socioeconomic data from the Brazilian Institute of Geography and Statistics (IBGE): including territorial area, population estimate, demographic density, Human Development Index (HDI), Gross Domestic Product (GDP), and monthly household income per capita. All indicators refer to the year 2020, except the HDI that refers to the year 2017. The data collection was carried out from February 23 to March 04, 2021. Our final dataset, named as BraCID, comprises: <strong>38,231</strong> Brazilian Goodreads users <strong>75,093</strong> Distinct books <strong>80</strong> Literary genres <strong>6</strong> IBGE indicators regarding the <strong>27</strong> federative units of Brazil
DOI: 10.6084/m9.figshare.14736489.v1
title: Boulder OSMP Climbing Formations

description: This GeoJSON contains the locations of rock climbing formations in Boulder County, Colorado.
DOI: 10.5517/ccdc.csd.cc27r3ns
title: CCDC 2077795: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.5517/ccdc.csd.cc27r3pt
title: CCDC 2077796: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.5517/ccdc.csd.cc27r3qv
title: CCDC 2077797: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.5517/ccdc.csd.cc27r3rw
title: CCDC 2077798: Experimental Crystal Structure Determination

description: Related Article: Sijing Xue, Bart Limburg, Debasish Ghorai, Jordi Benet-Buchholz, Arjan W Kleij|2021|Org.Lett.|23|4447|doi:10.1021/acs.orglett.1c01380
DOI: 10.5517/ccdc.csd.cc27whwj
title: CCDC 2082018: Experimental Crystal Structure Determination

description: Related Article: Yinsong Wu, Xinwei He, Mengqing Xie, Ruxue Li, Yi Ning, Jiahui Duan, Enshen Zhang, Yongjia Shang|2021|J.Org.Chem.|86|7370|doi:10.1021/acs.joc.1c00259
DOI: 10.5517/ccdc.csd.cc27whxk
title: CCDC 2082019: Experimental Crystal Structure Determination

description: Related Article: Yinsong Wu, Xinwei He, Mengqing Xie, Ruxue Li, Yi Ning, Jiahui Duan, Enshen Zhang, Yongjia Shang|2021|J.Org.Chem.|86|7370|doi:10.1021/acs.joc.1c00259
DOI: 10.5281/zenodo.4902044
title: NaturalProofs Dataset

description: The NaturalProofs Dataset is a multi-domain corpus for studying mathematical reasoning in natural language. NaturalProofs consists of roughly 30,000 theorem statements and proofs, 15,000 definitions, and 2,000 additional pages (e.g. axioms, corollaries), written in natural mathematical language. NaturalProofs contains broad-coverage data from ProofWiki, deep-coverage data from the Stacks project, and low-resource data from mathematics textbooks. NaturalProofs unifies these sources in a common schema and is made publicly available as a resource to drive progress on tasks involving informal mathematics. Please see the description in [Welleck et al 2021], and its corresponding GitHub repository for more details.
DOI: 10.5281/zenodo.4902189
title: NaturalProofs Dataset

description: The NaturalProofs Dataset is a multi-domain corpus for studying mathematical reasoning in natural language. NaturalProofs consists of roughly 30,000 theorem statements and proofs, 15,000 definitions, and 2,000 additional pages (e.g. axioms, corollaries), written in natural mathematical language. NaturalProofs contains broad-coverage data from ProofWiki, deep-coverage data from the Stacks project, and low-resource data from mathematics textbooks. NaturalProofs unifies these sources in a common schema and is made publicly available as a resource to drive progress on tasks involving informal mathematics. Please see the description in [Welleck et al 2021], and its corresponding GitHub repository for more details.
DOI: 10.5281/zenodo.4902202
title: NaturalProofs Dataset

description: The NaturalProofs Dataset is a multi-domain corpus for studying mathematical reasoning in natural language. NaturalProofs consists of roughly 30,000 theorem statements and proofs, 15,000 definitions, and 2,000 additional pages (e.g. axioms, corollaries), written in natural mathematical language. NaturalProofs contains broad-coverage data from ProofWiki, deep-coverage data from the Stacks project, and low-resource data from mathematics textbooks. NaturalProofs unifies these sources in a common schema and is made publicly available as a resource to drive progress on tasks involving informal mathematics. Please see the description in [Welleck et al 2021], and its corresponding GitHub repository for more details.
DOI: 10.6073/pasta/a8e93b56115178781b4fab946d857b09
title: Sawgrass Above and Below Ground Total Phosphorus from the Shark River Slough, Everglades National Park (FCE LTER), from September 2002 to Present

description: Once a year during the dry season (Dec-May) three live sawgrass plants are collected from each site. These plants are divided into their live above and live below ground parts. Then both the above and below ground parts are analyzed for TN, TC, and TP.
DOI: 10.5281/zenodo.4873909
title: Dependencies in DevOps Survey 2021

description: While various empirical studies on the application of DevOps in practice exist, the state of application dependencies and their impact on the order of deployments has not been assessed yet. Such insight would indicate whether independent, cross-functional DevOps teams may deploy their applications independently or whether they need to coordinate. Further, in case coordination is required, we do not yet have insight into how such coordination is accomplished. To fill this gap, we perform a cross-sectional, self-administered, online questionnaire survey with IT professionals. This report documents the survey until February 14, 2021, including the analysis of the collected data. It details and supplements the survey's presentation of our ESEC/FSE 2021 paper Automating Serverless Deployments for DevOps Organizations. Further, we provide the dataset and the scripts for the paper and this report. <strong>Contents</strong> README.md: Overview and instructions on how to use this artifact. report.pdf: Survey report providing documentation, results and analysis of the survey until February 14, 2021, detailing our paper Automating Serverless Deployments for DevOps Organizations. survey-data.csv: Survey dataset until February 14, 2021. analysis.zip: Analysis scripts for all statements and generation of all contents of report.pdf and our paper Automating Serverless Deployments for DevOps Organizations. Also includes the dataset and report.
DOI: 10.5519/qd.53kjodrd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (432 records)
DOI: 10.17632/5h9mdnjfkw.1
title: Data for: Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice

description: The file includes the data of the experiment described in Halko et. al. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice"
DOI: 10.17632/wsvbg63fns.2
title: Replication files for "From Exports to Value Added to Income: Accounting for Bilateral Income Transfers"

description: The replication folder contains all codes and data needed to replicate the empirical results in "From Exports to Value Added to Income: Accounting for Bilateral Income Transfers"
DOI: 10.5281/zenodo.4899927
title: ZeroWaste: Towards Automated Waste Recycling

description: Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem is the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Automated waste detection strategies have a great potential to enable more efficient, reliable and safer waste sorting practices, but the literature lacks comprehensive datasets and methodology for the industrial waste sorting solutions. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over 1800 fully segmented video frames collected from a real waste sorting plant along with waste material labels for training and evaluation of the segmentation methods, as well as over 6000 unlabeled frames that can be further used for semi-supervised and self-supervised learning techniques. ZeroWaste also provides frames of the conveyor belt before and after the sorting process, comprising a novel setup that can be used for weakly-supervised segmentation. We present baselines for fully-, semi- and weakly-supervised segmentation methods. The technical report, visualizations and code can be found at our website: http://ai.bu.edu/zerowaste/ .
DOI: 10.17632/zsvgtphcz4.1
title: Data for: Do Large-Scale Student Assessments Really Capture Cognitive Skills?

description: Two longitudinal data sets are constructed for this paper. The first panel is formed from ENLACE test scores of students who completed primary school (grade 6) in 2007, lower secondary school (grade 9) in 2010, and upper secondary school (grade 12) in 2013. The second panel merges a special module of the Mexican labor survey (ENOE, from the Spanish) applied to individuals ages 18, 19, and 20 years during the third quarter of 2010, with students that sat the ENLACE test in grade 12 in May 2008, 2009, and 2010.
DOI: 10.5281/zenodo.4902300
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.6084/m9.figshare.14736561.v1
title: H3K27me3 P8_sham mm10

description: For UCSC
DOI: 10.6084/m9.figshare.14736561
title: H3K27me3 P8_sham mm10

description: For UCSC