cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5539507
title: Replication package for Real Effects of Financial Distress: The Role of Heterogeneity

description: The package contains synthetic data and codes to replicate the figures and tables in the paper and appendix. Please refer to the readme file for further details.
DOI: 10.15468/fd5v6r
title: Two new species of the genus Gieysztoria (Rhabdocoela: Dalyelliidae) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhang, Hang, Li, Yi-Kui, Wu, Cheng-Chen, Wang, An-Tai (2014): Two new species of the genus Gieysztoria (Rhabdocoela: Dalyelliidae) from China. Zoological Systematics 39 (4): 485-495, DOI: 10.11865/zs.20140402
DOI: 10.6085/aa/shlx00_xxxitv2xlsr02_20191031.50.1
title: PISCO: Intertidal: site temperature data: Strawberry Hill, Oregon, USA (SHLX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Strawberry Hill, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2019-10-31. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.15472/r77dxh
title: Listado de las especies de Disteniidae (Coleoptera: Chrysomeloidea) de Colombia

description: Este recurso presenta el listado de las especies de la familia Disteniidae (Coleoptera: Chrysomeloidea) registradas para el territorio colombiano. Se listan tres tribus, 10 géneros y 37 especies. La información ha sido recopilada a partir de información disponible en los principales catálogos del grupo y en literatura reciente. Se incluyen las referencias de todas las descripciones originales y de los trabajos de revisión taxonómica o que incluyan claves de identificación.
DOI: 10.25345/c50s0f
title: MassIVE MSV000088171 - Titrating lysine acetylation for functional proteomics analyses
title: This submission contains the mass spectrometry files for the manuscript by Jeremie Loehr and Chahinez Noufi et al. that describes the establishment of a nutritional model allowing for the modulation of acetyl lysine levels and its coupling to functional proteomics analyses. AP-MS and proximity biotinylation experiments were performed from Flp-In T-REx HEK293 cells and MS files were acquired on Orbitrap Fusion mass spectrometer. Please refer to the individual Tables 1 for additional details of submitted files. For questions, please contact Jean-Philippe Lambert (Jean-Philippe.Lambert@crchudequebec.ulaval.ca).
DOI: 10.5517/ccdc.csd.cc25y53f
title: CCDC 2024024: Experimental Crystal Structure Determination

description: Related Article: Xiaopei Li, Dongdong Wang, Yongjie Zhang, Wenqi Lu, Songqiu Yang, Guangjin Hou, Zhenchao Zhao, Haijuan Qin, Yahui Zhang, Minmin Li, Guangyan Qing|2021|Chemical Science|12|12437|doi:10.1039/D1SC03765J
DOI: 10.5517/ccdc.csd.cc2718dz
title: CCDC 2056800: Experimental Crystal Structure Determination

description: Related Article: Bin Hua, Yanjun Ding, Lukman O. Alimi, Basem Moosa, Gengwu Zhang, Walaa S. Baslyman, Jonathan Sessler, Niveen M. Khashab|2021|Chemical Science|12|12286|doi:10.1039/D1SC03509F
DOI: 10.5517/ccdc.csd.cc2718f0
title: CCDC 2056801: Experimental Crystal Structure Determination

description: Related Article: Bin Hua, Yanjun Ding, Lukman O. Alimi, Basem Moosa, Gengwu Zhang, Walaa S. Baslyman, Jonathan Sessler, Niveen M. Khashab|2021|Chemical Science|12|12286|doi:10.1039/D1SC03509F
DOI: 10.5517/ccdc.csd.cc2718g1
title: CCDC 2056802: Experimental Crystal Structure Determination

description: Related Article: Bin Hua, Yanjun Ding, Lukman O. Alimi, Basem Moosa, Gengwu Zhang, Walaa S. Baslyman, Jonathan Sessler, Niveen M. Khashab|2021|Chemical Science|12|12286|doi:10.1039/D1SC03509F
DOI: 10.5517/ccdc.csd.cc27263n
title: CCDC 2057690: Experimental Crystal Structure Determination

description: Related Article: Margarita López-Espejel, Alberto Gómez-Treviño, Blanca M. Muñoz-Flores, Manuel A. Treto-Suarez, Eduardo Schott, Dayán Páez-Hernández, Ximena Zarate, Víctor M. Jiménez-Pérez|2021|J.Mater.Chem.B|9|7698|doi:10.1039/D1TB01405F
DOI: 10.5517/ccdc.csd.cc2718cy
title: CCDC 2056799: Experimental Crystal Structure Determination

description: Related Article: Bin Hua, Yanjun Ding, Lukman O. Alimi, Basem Moosa, Gengwu Zhang, Walaa S. Baslyman, Jonathan Sessler, Niveen M. Khashab|2021|Chemical Science|12|12286|doi:10.1039/D1SC03509F
DOI: 10.5517/ccdc.csd.cc279rjv
title: CCDC 2064957: Experimental Crystal Structure Determination

description: Related Article: Nivedita Chaudhri, Matthew J. Guberman-Pfeffer, Ruoshi Li, Matthias Zeller, Christian Brückner|2021|Chemical Science|12|12292|doi:10.1039/D1SC03403K
DOI: 10.5517/ccdc.csd.cc279rkw
title: CCDC 2064958: Experimental Crystal Structure Determination

description: Related Article: Nivedita Chaudhri, Matthew J. Guberman-Pfeffer, Ruoshi Li, Matthias Zeller, Christian Brückner|2021|Chemical Science|12|12292|doi:10.1039/D1SC03403K
DOI: 10.5517/ccdc.csd.cc279rlx
title: CCDC 2064959: Experimental Crystal Structure Determination

description: Related Article: Nivedita Chaudhri, Matthew J. Guberman-Pfeffer, Ruoshi Li, Matthias Zeller, Christian Brückner|2021|Chemical Science|12|12292|doi:10.1039/D1SC03403K
DOI: 10.5517/ccdc.csd.cc27lp6r
title: CCDC 2073534: Experimental Crystal Structure Determination

description: Related Article: Qiucheng Chen|2021|CSD Communication|||
description: Related Article: Qiu-Cheng Chen, Natalia Fridman, Boris L. Tumanskii, Zeev Gross|2021|Chemical Science|12|12445|doi:10.1039/D1SC02593G
DOI: 10.5517/ccdc.csd.cc27ry0z
title: CCDC 2078581: Experimental Crystal Structure Determination

description: Related Article: Isabelle Georg, Markus Bursch, Burkhard Endeward, Michael Bolte, Hans-Wolfram Lerner, Stefan Grimme, Matthias Wagner|2021|Chemical Science|12|12419|doi:10.1039/D1SC03958J
DOI: 10.5517/ccdc.csd.cc27t9jx
title: CCDC 2079899: Experimental Crystal Structure Determination

description: Related Article: Bin Hua, Yanjun Ding, Lukman O. Alimi, Basem Moosa, Gengwu Zhang, Walaa S. Baslyman, Jonathan Sessler, Niveen M. Khashab|2021|Chemical Science|12|12286|doi:10.1039/D1SC03509F
DOI: 10.5517/ccdc.csd.cc28fryf
title: CCDC 2098605: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28frzg
title: CCDC 2098606: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28fs0j
title: CCDC 2098607: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28fs1k
title: CCDC 2098608: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28fs2l
title: CCDC 2098609: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28fs3m
title: CCDC 2098610: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5517/ccdc.csd.cc28fs4n
title: CCDC 2098611: Experimental Crystal Structure Determination

description: Related Article: A.A. Gaydamaka, S.G. Arkhipov, E.V. Boldyreva|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|808|doi:10.1107/S205252062100754X
DOI: 10.5281/zenodo.5539703
title: CODE-AE-extra-data

description: The data used for CODE-AE biclustering analysis
DOI: 10.5281/zenodo.5539702
title: CODE-AE-extra-data

description: The data used for CODE-AE biclustering analysis
DOI: 10.25345/c5vz7k
title: MassIVE Reanalysis RMSV000000256.247 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Agrobacterium tumefaciens IAM 12048 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Agrobacterium tumefaciens IAM 12048 (MSV000083150/PXD010000).
DOI: 10.6085/aa/shlx00_xxxitv2xlsr03_20191031.50.1
title: PISCO: Intertidal: site temperature data: Strawberry Hill, Oregon, USA (SHLX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Strawberry Hill, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2019-10-31. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.25345/c5r850
title: MassIVE Reanalysis RMSV000000256.248 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bacillus subtilis 168 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bacillus subtilis 168 (MSV000083150/PXD010000).
DOI: 10.25345/c5mg2p
title: MassIVE Reanalysis RMSV000000256.249 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Alcaligenes faecalis from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Alcaligenes faecalis (MSV000083150/PXD010000).
DOI: 10.25345/c5c26w
title: MassIVE Reanalysis RMSV000000256.251 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bacillus cereus ATCC14579 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bacillus cereus ATCC14579 (MSV000083150/PXD010000).
DOI: 10.25345/c5gs0s
title: MassIVE Reanalysis RMSV000000256.250 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Anaerococcus hydrogenalis DSM 7454 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Anaerococcus hydrogenalis DSM 7454 (MSV000083150/PXD010000).
DOI: 10.25345/c5785b
title: MassIVE Reanalysis RMSV000000256.253 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bacteroides fragilis 638R from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bacteroides fragilis 638R (MSV000083150/PXD010000).
DOI: 10.25345/c53k1p
title: MassIVE Reanalysis RMSV000000256.254 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bifidobacterium longum infantis ATCC15697 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bifidobacterium longum infantis ATCC15697 (MSV000083150/PXD010000).
DOI: 10.25345/c5zr96
title: MassIVE Reanalysis RMSV000000256.256 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bifidobacterium bifidum ATCC29521 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bifidobacterium bifidum ATCC29521 (MSV000083150/PXD010000).
DOI: 10.25345/c5v27m
title: MassIVE Reanalysis RMSV000000256.252 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Algoriphagus marincola HL-49 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Algoriphagus marincola HL-49 (MSV000083150/PXD010000).
DOI: 10.25345/c5q849
title: MassIVE Reanalysis RMSV000000256.257 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Cellulophaga baltica 18 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Cellulophaga baltica 18 (MSV000083150/PXD010000).
DOI: 10.21223/1j1ydl/h0dk8q
title: 02_dictionary.tab
DOI: 10.21223/1j1ydl/twr3zr
title: 01_Sweetpotato_Vine_Silage_Dataset.tab

description: :unav
DOI: 10.21223/1j1ydl
title: Dataset for: Dataset for Determination of smallholder dairy farmers feeding practices and their perceptions on the use of sweetpotato vines silage (SPVS) as a feed resource in their production cycle.

description: Smallholder dairy farmers constitute up to 85% of dairy farmers in Uganda (Atuhaire, 2014). However, this category of farmers is challenged and faced by unstable feed quality and quantity supply year-round (Kiggundu et al., 2014). This therefore has led to unstable milk production and productivity which has dare effects on the milk prices and incomes of this category of dairy farmers. Studies have indicated that crop residues have a high potential to bridge the feed gap and provide a cheaper option to maintain a stable feed supply when well preserved (Dione et al., 2015). A study funded by International Potato Center (CIP) was conducted in three districts of Wakaiso, Masaka, and Kamuli to determine prevalent smallholder dairy farmers feeding practices and their perceptions on the use of sweetpotato vines silage (SPVS) as a feed resource in their production cycle. The local government structures were used to select potential farmer respondents. A list of smallholder dairy farmers in a district was obtained from the District Veterinary Officer (DVO), five sub-counties in Wakiso and two each from Masaka and Kamuli were selected based on the population density of the smallholder dairy farmers in these districts. Systematically using an interval of five, farmers were selected and a structured questionnaire designed in the CSEntry data tool was administered to the selected farmers. A total of 147 farmers with the help of a local language translator were interviewed, on-farm observations were made, data were screened for any misrepresentations and follow-up questions were posed and saved. Preliminary findings during the study indicated that zero-grazing (58.59%) is most commonly practiced. Farmers reported contamination of sweetpotato vines with soil that hinders the use of the feed resource for feeding livestock. Significant amounts of vines are lost by some farmers after being brought to the farm for the luck of chopping and other forms of feed manipulations to facilitate intake. The majority of women (76.2%) and men (80.9%) agree to the statement “I have a say over how household income from dairy products is spent” Data analysis is underway and a complete report will be produced documenting findings from the study. Financial support from (CIP) is highly appreciated.
DOI: 10.15139/s3/djpfm1
title: The prevalence, grouping, and distribution of stressors and their association with anxiety among hospitalized patients

description: This dataset stores data collected for a study examining stressors endorsed by inpatients in two acute-care hospitals: Emory University Hospital and Emory University Hospital-Midtown and anxiety as measured by the State-Trait Anxiety Index 6-item instrument (STAI-6) developed my Marteau and Bekker in 1992.
DOI: 10.25345/c5kk2d
title: MassIVE Reanalysis RMSV000000256.255 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bacillus subtilis NCIB3610 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bacillus subtilis NCIB3610 (MSV000083150/PXD010000).
DOI: 10.6084/m9.figshare.16700041.v1
title: Ch2 Ecotype Project [Data + R code]

description: Grassland restoration unveils phylogenetic signal and community assembly along a longitudinal aridity gradient - raw data and R code archive
DOI: 10.15468/gs7cy5
title: Five new species of Phyllocoptinae (Acari: Eriophyoidea: Eriophyidae) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ou, Shan-Sheng, Wang, Guo-Quan, Wei, Sui-Gai (2014): Five new species of Phyllocoptinae (Acari: Eriophyoidea: Eriophyidae) from China. Zoological Systematics 39 (4): 496-506, DOI: 10.11865/zs.20140403
DOI: 10.25345/c5fr85
title: MassIVE Reanalysis RMSV000000256.258 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Chryseobacterium indologenes from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Chryseobacterium indologenes (MSV000083150/PXD010000).
DOI: 10.25345/c5b27z
title: MassIVE Reanalysis RMSV000000256.259 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Cellulomonas gilvus ATCC13127 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Cellulomonas gilvus ATCC13127 (MSV000083150/PXD010000).
DOI: 10.25345/c56838
title: MassIVE Reanalysis RMSV000000256.260 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Campylobacter jejuni from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Campylobacter jejuni (MSV000083150/PXD010000).
DOI: 10.25345/c52k2r
title: MassIVE Reanalysis RMSV000000256.261 - CCMS ProteoCarta spectral library from MS-GF+ reanalysis of Bacteroides thetaiotaomicron VPI-5482 from DeNovo Peptide Identification Deep Learning Training Set [launched by cc1a3065088c4261a2d6cd271d16674a]
title: CCMS ProteoCarta spectral library from reanalysis of Bacteroides thetaiotaomicron VPI-5482 (MSV000083150/PXD010000).
DOI: 10.5281/zenodo.5539789
title: Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

description: The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.
DOI: 10.5281/zenodo.5539788
title: Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components

description: The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.
DOI: 10.3886/e130502
title: Data and Code for: From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising

description: This project contains the data and replication file for: "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising" <br>Abstract: This paper analyzes the impact of intermediary concentration on the allocation of revenue in online platforms. We study {sponsored search} documenting how advertisers increasingly bid through a handful of specialized intermediaries. This enhances automated bidding and data pooling, but lessens competition whenever the intermediary represents competing advertisers. Using data on nearly 40 million Google keyword-auctions, we first apply machine learning algorithms to cluster keywords into thematic groups serving as relevant markets. Using an instrumental variable strategy, we estimate a decline in the platform's revenue of approximately 11 percent due to the average rise in concentration associated with intermediary M&A activity.<br><br><br><br>
description: Keyword auctions on Google sponsored searches US involving the universe of advertisers part of the Redbooks directory<br>.
DOI: 10.3886/e130502v1
title: Data and Code for: From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising

description: This project contains the data and replication file for: "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising" <br>Abstract: This paper analyzes the impact of intermediary concentration on the allocation of revenue in online platforms. We study {sponsored search} documenting how advertisers increasingly bid through a handful of specialized intermediaries. This enhances automated bidding and data pooling, but lessens competition whenever the intermediary represents competing advertisers. Using data on nearly 40 million Google keyword-auctions, we first apply machine learning algorithms to cluster keywords into thematic groups serving as relevant markets. Using an instrumental variable strategy, we estimate a decline in the platform's revenue of approximately 11 percent due to the average rise in concentration associated with intermediary M&A activity.<br><br><br><br>
description: Keyword auctions on Google sponsored searches US involving the universe of advertisers part of the Redbooks directory<br>.
DOI: 10.5281/zenodo.5148058
title: Measuring Disagreement in Science

description: This data set contains data used in the project <em>Measuring Disagreement in Science</em> documented in the following preprint: arXiv:2107.14641. The data set contains the following data: The 65 disagreement queries (signal phrase, filter phrase, valid or not, number of citing sentences returned by the query, SQL query). Of the 65 queries, 23 passed our 80% validity threshold. The 455,625 citing sentences identified by the 23 validated disagreement queries (DOI, sentence sequence number, queries by which the sentence was identified, sentence text). The publications in which the citing sentences occur (DOI, publication year, number of sentences in the main text, main field to which the publication was assigned, meso-level field to which the publication was assigned, inferred gender of the first and last author). The data was retrieved through the Elsevier ScienceDirect API subject to Elsevier’s policy for text and data mining.
DOI: 10.5281/zenodo.5148057
title: Measuring Disagreement in Science

description: This data set contains data used in the project <em>Measuring Disagreement in Science</em> documented in the following preprint: arXiv:2107.14641. The data set contains the following data: The 65 disagreement queries (signal phrase, filter phrase, valid or not, number of citing sentences returned by the query, SQL query). Of the 65 queries, 23 passed our 80% validity threshold. The 455,625 citing sentences identified by the 23 validated disagreement queries (DOI, sentence sequence number, queries by which the sentence was identified, sentence text). The publications in which the citing sentences occur (DOI, publication year, number of sentences in the main text, main field to which the publication was assigned, meso-level field to which the publication was assigned, inferred gender of the first and last author). The data was retrieved through the Elsevier ScienceDirect API subject to Elsevier’s policy for text and data mining.