cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7270/q2hd7xqn
title: BindingDB Entry 1328: Tricyclic compounds as inhibitors of immunosuppression mediated by tryptophan metabolization

description: Small molecule binding data for protein Indoleamine 2,3-dioxygenase 1 and Tryptophan 2,3-dioxygenase, Uniprot P14902 and P48775
DOI: 10.7270/q2jd4zwg
title: BindingDB Entry 1316: Pyrrolo[2,3-d]pyrimidinyl, pyrrolo[2,3-b]pyrazinyl and pyrrolo[2,3-d]pyridinyl acrylamides

description: Small molecule binding data for protein Tyrosine-protein kinase JAK3, Uniprot P52333
DOI: 10.7270/q2kd20xw
title: BindingDB Entry 291: Fused bicyclic 1,2,4-triazine compounds as TAM inhibitors

description: Small molecule binding data for protein MER intracellular domain/EGFR extracellular domain chimera, Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit delta isoform/gamma isoform, Tyrosine-protein kinase JAK1, Tyrosine-protein kinase receptor TYRO3, and Tyrosine-protein kinase receptor UFO, Uniprot O00329, P23458, P30530, Q06418, and Q12866
DOI: 10.7270/q2mc921p
title: BindingDB Entry 273: 2-acylaminothiazoles for the treatment of cancer

description: Small molecule binding data for protein ALK tyrosine kinase receptor (L1196M) and EML4-ALK , Uniprot Q9UM73
DOI: 10.7270/q2n58pfc
title: BindingDB Entry 1327: Triazolo compounds

description: Small molecule binding data for protein cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A, Uniprot Q9Y233
DOI: 10.7270/q2q52rn7
title: BindingDB Entry 280: Sulfonimidoylpurinone compounds and derivatives for the treatment and prophylaxis of virus infection

description: Small molecule binding data for protein Toll-like receptor 7 (TLR7), Uniprot Q9NYK1
DOI: 10.7270/q2r49ss2
title: BindingDB Entry 272: Heterocyclic compounds and methods for their use

description: Small molecule binding data for protein Angiotensin II receptor, Uniprot P50052
DOI: 10.7270/q2rx9f4b
title: BindingDB Entry 1326: N-substituted pyrazolo [3,4-D] pyrimidine ketone compound, and preparation process and use thereof

description: Small molecule binding data for protein Phosphodiesterase 9A, Uniprot O76083
DOI: 10.7270/q2tx3hd3
title: BindingDB Entry 279: Bicyclic heterocycles as FGFR4 inhibitors

description: Small molecule binding data for protein Fibroblast growth factor receptor 1, Fibroblast growth factor receptor 2, Fibroblast growth factor receptor 3, and Fibroblast growth factor receptor 4, Uniprot P11362, P21802, P22455, and P22607
DOI: 10.7270/q2wm1ghs
title: BindingDB Entry 1324: Compounds and methods for kinase modulation, and indications therefor

description: Small molecule binding data for protein Cytochrome P450 1A2, Cytochrome P450 2C19, Cytochrome P450 2C9, Cytochrome P450 2D6, Cytochrome P450 3A, Epidermal growth factor receptor(L858R, T790M), RAF proto-oncogene serine/threonine-protein kinase (RAF-1), and bRAF serine/threonine protein kinase, Uniprot P05177, P08684, P10635, P11712, P15056, and P33261
DOI: 10.7270/q2zp4858
title: BindingDB Entry 278: Substituted pyrido[3,4-b]pyrazines as GPR6 modulators

description: Small molecule binding data for protein G-protein coupled receptor 6 (GPR6), Uniprot F1DAM6 and P46095
DOI: 10.17632/3tfh8g8kpd.3
title: Using Eye-tracking Technology to Identify Visualizers and Verbalizers- a quasi-experiment

description: This is an eye-tracking experiment to investigate whether visualizers and verbalizers present different eye-movement patterns in viewing documents with different arrangements of images and text. Combined with the questionnaire and interview approach, the current study explored the accuracy of using eye-tracking technology to identify visualizers and verbalizers, as well as possible factors contributing to different levels of accuracy.
DOI: 10.17632/4sw5hr2yz7.1
title: Dataset for QSAR Modeling of DPP-4 Inhibitors

description: The data that used for DPP-4 inhibitors QSAR modeling with machine learning methods were raw data from ChEMBL, initial processing data, and data partitioning for modeling which is divided into 3 parts i.e. training data, validation data (internal validation) and data test (external validation). This dataset also contained an explanation of the methods and tools used for modeling, and the predicted results of pIC50 values from the best model (from external validation data).
DOI: 10.17632/jgjgz48hgb.1
title: Seismic tomographic models of "Structural heterogeneity in source zones of the 2018 Anchorage intraslab earthquake and the 1964 Alaska megathrust earthquake"

description: 3-D seismic structures (Vp, Vs and Poisson’s ratio) in and around the source zones of the 2018 Anchorage intraslab earthquake (Mw 7.1) and the 1964 Alaska megathrust earthquake (Mw 9.2)
DOI: 10.17632/s8f5tpxth4.1
title: Sakaguchi T et al. Figure 4B

description: Nuclear fractions extracted from LPS-stimulated MZ B cells immunoblotted with anti-RelA antibody
DOI: 10.17632/s8f5tpxth4.2
title: TRPM5 Negatively Regulates Ca2+-Dependent Responses in Lipopolysaccharide-Stimulated B Lymphocytes

description: Nuclear fractions extracted from LPS-stimulated MZ B cells immunoblotted with anti-RelA antibody
DOI: 10.17632/ttprc6d3hg.2
title: Data for: Suspended sediment fluxes in a shallow macrotidal estuary

description: Timeseries of water level, tidal velocity, backscatter, suspended sediment concentration, and sediment flux derived from acoustic doppler current profilers. Sediment fluxes calculated according to Dyer decomposition equation and Yu et al. (2012) analytical model. Two deployments in 2013: February and September. Two locations in the estuary: Site 1 near the mouth of the river (48° 16.842’ N, 004° 16.009’ W), and Site 3 in the central estuary (48° 14.851’ N, 004° 10.140’ W). Yu, Q., Wang, Y.P., Flemming, B., Gao, S., 2012. Tide-induced suspended sediment transport: Depth-averaged concentrations and residual fluxes. Continental Shelf Research 34, 53-63.
DOI: 10.7270/q20c4xv9
title: BindingDB Entry 1332: Nuclear receptor binding agents

description: Small molecule binding data for protein Estrogen receptor alpha and Estrogen receptor beta, Uniprot P03372 and Q92731
DOI: 10.7270/q2445pjk
title: BindingDB Entry 1331: Macrocyclic compounds as IRAK4 inhibitors for the treatment of inflammatory diseases

description: Small molecule binding data for protein Interleukin-1 receptor-associated kinase 4, Uniprot Q9NWZ3
DOI: 10.7270/q27w6f7j
title: BindingDB Entry 1330: Imidazo[2,1-b]thiazole derivatives, their preparation and use as medicaments

description: Small molecule binding data for protein Sigma non-opioid intracellular receptor 1, Uniprot Q60492
DOI: 10.7270/q2bp04vg
title: BindingDB Entry 1345: Heteroaryl compounds as sodium channel blockers

description: Small molecule binding data for protein Sodium channel protein type 9 subunit alpha, Uniprot Q15858
DOI: 10.7270/q2cn75zv
title: BindingDB Entry 1329: Fused tetra or penta-cyclic dihydrodiazepinocarbazolones as PARP inhibitors

description: Small molecule binding data for protein B-RAF V600E, Poly [ADP-ribose] polymerase 1, Poly [ADP-ribose] polymerase 2 (PARP2), and Poly [ADP-ribose] polymerase tankyrase-2 (TANK2), Uniprot P09874, Q9H2K2, and Q9UGN5
DOI: 10.7270/q2gf0wkt
title: BindingDB Entry 1343: IDO inhibitors

description: Small molecule binding data for protein Indoleamine 2,3-dioxygenase 1, Uniprot P14902
DOI: 10.7270/q2m61nb0
title: BindingDB Entry 1338: Fused-bicyclic aryl quinolinone derivatives as mutant-isocitrate dehydrogenase inhibitors

description: Small molecule binding data for protein IDH1 R132H and Isocitrate dehydrogenase [NADP] cytoplasmic (IDH1)(R132C)
DOI: 10.7270/q2qz2d2d
title: BindingDB Entry 1337: Aniline derivatives, their preparation and their therapeutic application

description: Small molecule binding data for protein Prostaglandin E2 receptor EP4 subtype, Uniprot P35408
DOI: 10.7270/q2vq34r8
title: BindingDB Entry 1333: Methods of treating a cancer using substituted pyrrolopyrimidine compounds, compositions thereof

description: Small molecule binding data for protein CaM-kinase kinase beta, Dual specificity protein kinase CLK2, Dual specificity protein kinase TTK, and Dual specificty protein kinase CLK1, Uniprot P33981, P49759, P49760, and Q96RR4
DOI: 10.17632/4gyydjb4y9.1
title: The mammals of the Palaeo-Agulhas Plain
DOI: 10.17632/4sw5hr2yz7.2
title: Dataset for QSAR Modeling of DPP-4 Inhibitors

description: The data that used for DPP-4 inhibitors QSAR modeling with machine learning methods were raw data from ChEMBL, initial processing data, and data partitioning for modeling which is divided into 3 parts i.e. training data, validation data (internal validation) and data test (external validation). This dataset also contained an explanation of the methods and tools used for modeling, and the predicted results of pIC50 values from the best model (from external validation data).
DOI: 10.17632/7zk5gdwdkn.1
title: Output_GLM_GMM_Article_ForestResilience_Post_Fire

description: Output data analysis in SPSS Software of GLM and GMM Article: FOREST RESILIENCE TO FIRE IN EASTERN AMAZON DEPENDS ON THE INTENSITY OF PRE-FIRE DISTURBANCE
DOI: 10.17632/y5nbwtzf8v.1
title: The Incidence of Vote Buying in Nigeria's 2019 Presidential Elections and the impact on democracy

description: This work examines the incidence of Vote buying during the 2019 Presidential Elections in Nigeria and the Impact on Democracy. It adopts the clientelist theory and employed the survey method of research design in which the data required for the study were generated through the instruments of questionnaire and in-depth interviews. Quota and stratified sampling techniques were used in the selection of respondents. Eight Hundred copies of questionnaire were administered across the six geo-political zones of the country covering twenty four of the thirty six states and Federal Capital Territory, Abuja and six hundred and ninety nine (699) were returned out of which six hundred (600) were found useable. The data were analyzed using ordinary linear regression analysis, inferential statistics, tables, frequencies, and percentages to crystallize and present the results. The results of the study showed that vote buying thrives in emerging democracies like Nigeria where accountability of the ruling class to the masses is non-existent and the ruling class cannot be trusted by the masses to live up to the promises made to them prior to elections hence they demand upfront for their own part of the dividends of democracy by selling their votes to the highest bidders.
DOI: 10.5281/zenodo.3526331
title: Neutron diffraction data of Sikkim and West Bengal samples

description: This is the data set for the quartz pole figures for six quartzite samples from Sikkim and West Bengal, Indian Himalayas. The data for each sample starts with the sample label: SK222, WB03, WB10, WB11, WB41, and WB76.
DOI: 10.5281/zenodo.3526332
title: Neutron diffraction data of Sikkim and West Bengal samples

description: This is the data set for the quartz pole figures for six quartzite samples from Sikkim and West Bengal, Indian Himalayas. The data for each sample starts with the sample label: SK222, WB03, WB10, WB11, WB41, and WB76.
DOI: 10.7270/q2377bs2
title: BindingDB Entry 1352: Substituted piperidinyltetrahydroquinolines

description: Small molecule binding data for protein Alpha-1A adrenergic receptor, Uniprot P35348
DOI: 10.7270/q26w9d52
title: BindingDB Entry 1346: 2-pyridylamino-4-nitrile-piperidinyl orexin receptor antagonists

description: Small molecule binding data for protein Orexin receptor type 1 and Orexin receptor type 2, Uniprot O43613 and O43614
DOI: 10.7270/q29p33rj
title: BindingDB Entry 1358: Dihydropyrrolopyridine inhibitors of ROR-gamma

description: Small molecule binding data for protein Nuclear receptor ROR-gamma, Uniprot P51449
DOI: 10.7270/q2ff3vff
title: BindingDB Entry 1357: Compounds and methods for kinase modulation, and indications therefor

description: Small molecule binding data for protein B-RAF V600E
DOI: 10.7270/q2k64m55
title: BindingDB Entry 1356: Amino-pyrimidine-containing spleen tyrosine kinase (Syk) inhibitors

description: Small molecule binding data for protein Tyrosine-protein kinase SYK, Uniprot P43405
DOI: 10.7270/q2pz5bxm
title: BindingDB Entry 1355: Substituted 6,5-fused bicyclic heteroaryl compounds

description: Small molecule binding data for protein EZH2(Y641F) and Histone-lysine N-methyltransferase EZH2, Uniprot Q15910
DOI: 10.7270/q2tq63nb
title: BindingDB Entry 1354: Inhibitors of bruton's tyrosine kinase

description: Small molecule binding data for protein Tyrosine-protein kinase BTK, Uniprot Q06187
DOI: 10.7270/q2zg6vb7
title: BindingDB Entry 1353: Substituted bipiperidinyl derivatives

description: Small molecule binding data for protein Alpha-1A adrenergic receptor, Uniprot P35348
DOI: 10.17632/kztxvph8m2.2
title: Data for: Physiological and perceptual correlates of masculinity in children’s voices

description: Dataset for "Physiological and perceptual correlates of masculinity in children’s voices " study
DOI: 10.17632/t98c6t6ccr.1
title: Geographic data Visualisation and Map Generation

description: This project is one of the academic projects given to us in the Geographic Information System (GIS) Course. Created by: Pranav Pandya (Me) and Kartikey Hadiya We sampled information for pollution emission in Delhi, India. Pollution data was obtained from https://data.gov.in/resources/real-time-air-quality-index-various-locations Pollution index data can be obtained from https://cpcb.nic.in/RealTimeAirQualityData.php Pollution data only had address of Indian Meteorological Department, so each station was located in Google Earth and pin points were added at each station. Then in the sidebar containing those pins on right-click, a new folder was added and all the pins were added in that new folder in google earth. Then that folder was saved as kml file. This kml file was uploaded to Mygeodata: https://mygeodata.cloud/converter/kml-to-csv and was converted into csv. Then the csv file was opened and coordinates were copied in the pollution data file. That file was later saved as CSV and imported in ArcGIS and xy data was displayed. Shapefile was obtained from web search, which is attached as well. That shapefile was imported in ArcGIS and the final view was generated which is shown in the picture.
DOI: 10.18739/a2348gg50
title: Snow water equivalent data from the Upper Kuparuk River watershed, Arctic Alaska, 1997-2017

description: Many remote areas of Alaska lack meteorological data; this is especially true for solid precipitation. Researchers at the University of Alaska Fairbanks, Water and Environmental Research Center have been collecting end-of-winter snow depth, snow density, and snow water equivalent (SWE) during annual snow surveys. Snow surveys in the Upper Kuparuk watershed (130 square kilometers) provide ground-based spatially distributed snow water equivalent (SWE) measurements from 1997 to 2017. These observations do not document the total snowfall during the winter, but provide quantitative estimate of cold season precipitation on the ground at winter’s end after sublimation and redistribution by wind.
DOI: 10.3886/e112162
title: Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities

description: Please cite the corresponding article if this dataset is used to support additional findings:<br><br>Leon-Moreta, A. "Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities." Urban Affairs Review (2019): 1-34. doi:10.1177/1078087419884732.<br>The data described here were used in the analysis and findings reported in the article, Leon-Moreta, A. "Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities." Urban Affairs Review (2019): 1-34. doi:10.1177/1078087419884732. A STATA (V. 13) do-file for calculating variables is also included. Please cite the corresponding article if this dataset is used to support additional findings.<br><br>https://journals.sagepub.com/doi/10.1177/1078087419884732<br><br>Cited Datasets:<br><br>The dataset described here cites data from the following sources:<br><br>Bureau of Labor Statistics. 2002–2012. Consumer Price Index. https://www.bls.gov (accessed March 27, 2018).<br>Berry, William, Evan Ringquist, Richard Fording, and Russell Hanson. 1998. “Measuring Citizen and Government Ideology in the American States, 1960-93.” American Journal of Political Science 42(1): 327-348.<br><br>Census Bureau. 1987–2012. Census of Governments. Washington, DC: Department of Commerce.<br>Census Bureau. 2000. Census of Population and Housing. Washington, DC: Department of Commerce.<br>Census Bureau. 2005–2014. American Community Survey. Washington, DC: Department of Commerce.<br>Census Bureau. 1988–2011. Boundary and Annexation Survey. Washington, DC: Department of Commerce.<br>International City/County Management Association. 1996–2011. Municipal Form of Government Surveys. Washington, DC: ICMA.<br>National Center for Charitable Statistics. 2000–2010. IRS Business Master File. Washington, DC: Urban Institute.<br>National Historical Geographic Information System. 2001–2011. National Land Cover Database. Minneapolis, MN: University of Minnesota.<br>Trust for Public Land. 2018. LandVote®. Landvote.org (accessed March 27, 2018).<br>Trust for Public Land. 2000, 2005, 2010. National Conservation Easement Database.<br>National Agricultural Statistics Service. Census of Agriculture. Washington, DC: United States Department of Agriculture.<br>Woods & Poole. 2000 through 2010. MSA Profile. Washington, DC: Woods & Poole.<br>
description: Municipalities in the United States
DOI: 10.3886/e112162v1
title: Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities

description: Please cite the corresponding article if this dataset is used to support additional findings:<br><br>Leon-Moreta, A. "Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities." Urban Affairs Review (2019): 1-34. doi:10.1177/1078087419884732.<br>The data described here were used in the analysis and findings reported in the article, Leon-Moreta, A. "Ballot Measures for Open Space Conservation: Economic and Institutional Processes in Cities." Urban Affairs Review (2019): 1-34. doi:10.1177/1078087419884732. A STATA (V. 13) do-file for calculating variables is also included. Please cite the corresponding article if this dataset is used to support additional findings.<br><br>https://journals.sagepub.com/doi/10.1177/1078087419884732<br><br>Cited Datasets:<br><br>The dataset described here cites data from the following sources:<br><br>Bureau of Labor Statistics. 2002–2012. Consumer Price Index. https://www.bls.gov (accessed March 27, 2018).<br>Berry, William, Evan Ringquist, Richard Fording, and Russell Hanson. 1998. “Measuring Citizen and Government Ideology in the American States, 1960-93.” American Journal of Political Science 42(1): 327-348.<br><br>Census Bureau. 1987–2012. Census of Governments. Washington, DC: Department of Commerce.<br>Census Bureau. 2000. Census of Population and Housing. Washington, DC: Department of Commerce.<br>Census Bureau. 2005–2014. American Community Survey. Washington, DC: Department of Commerce.<br>Census Bureau. 1988–2011. Boundary and Annexation Survey. Washington, DC: Department of Commerce.<br>International City/County Management Association. 1996–2011. Municipal Form of Government Surveys. Washington, DC: ICMA.<br>National Center for Charitable Statistics. 2000–2010. IRS Business Master File. Washington, DC: Urban Institute.<br>National Historical Geographic Information System. 2001–2011. National Land Cover Database. Minneapolis, MN: University of Minnesota.<br>Trust for Public Land. 2018. LandVote®. Landvote.org (accessed March 27, 2018).<br>Trust for Public Land. 2000, 2005, 2010. National Conservation Easement Database.<br>National Agricultural Statistics Service. Census of Agriculture. Washington, DC: United States Department of Agriculture.<br>Woods & Poole. 2000 through 2010. MSA Profile. Washington, DC: Woods & Poole.<br>
description: Municipalities in the United States
DOI: 10.5281/zenodo.3528250
title: Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset

description: High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web. <strong>November, 4 2019</strong> - Added a spreadsheet for the full internal version of ASR datasets (<em>N</em> = 14,142). - Moved additional disordered structures to disorder folder (thanks to Matthew Witman and Martin Hutereau) <strong>August, 17 2019</strong> - FIJDOS_freeONLY has been corrected.
DOI: 10.7270/q21838m6
title: BindingDB Entry 1371: Aminoindazole derivatives as sodium channel inhibitors

description: Small molecule binding data for protein Sodium channel protein type 5 subunit alpha and Sodium channel protein type 9 subunit alpha, Uniprot Q14524 and Q15858
DOI: 10.7270/q22809pp
title: BindingDB Entry 1361: Inhibitors of ERK and methods of use

description: Small molecule binding data for protein Mitogen-activated protein kinase 1, Uniprot P28482
DOI: 10.7270/q25141bx
title: BindingDB Entry 1370: Substituted quinazolines for inhibiting kinase activity

description: Small molecule binding data for protein Epidermal growth factor receptor, Epidermal growth factor receptor(L858R, T790M), Fibroblast growth factor receptor 1, Fibroblast growth factor receptor 2, Tyrosine-protein kinase BTK, Tyrosine-protein kinase JAK3, and Vascular endothelial growth factor receptor 2 and tyrosine-protein kinase TIE-2 (KDR and TIE2), Uniprot P00533, P11362, P21802, P35968, P52333, and Q06187
DOI: 10.7270/q26112cz
title: BindingDB Entry 1359: Pyrido[2,3-d]pyrimidin-4-one compounds as tankyrase inhibitors

description: Small molecule binding data for protein Poly [ADP-ribose] polymerase 1, Poly [ADP-ribose] polymerase tankyrase-2 (TNKS2), and Tankyrase 1 (TNKS1), Uniprot O95271, P09874, and Q9H2K2
DOI: 10.7270/q28s4s02
title: BindingDB Entry 1369: Pyrazolo-pyrrolidin-4-one derivatives as bet inhibitors and their use in the treatment of disease

description: Small molecule binding data for protein Bromodomain-containing protein 2, Bromodomain-containing protein 3, Bromodomain-containing protein 4, and CREB-binding protein, Uniprot O60885, P25440, Q15059, and Q92793
DOI: 10.7270/q2df6t92
title: BindingDB Entry 1367: Pyridazinone macrocycles as IRAK inhibitors and uses thereof

description: Small molecule binding data for protein Interleukin-1 receptor-associated kinase 1 (IRAK1) and Interleukin-1 receptor-associated kinase 4, Uniprot Q9NWZ3
DOI: 10.7270/q2j67k0b
title: BindingDB Entry 1366: Heterocyclic compounds and methods of their use

description: Small molecule binding data for protein Angiotensin II receptor, Uniprot P50052
DOI: 10.7270/q2nz89qb
title: BindingDB Entry 1365: Tricyclic heterocycles as BET protein inhibitors

description: Small molecule binding data for protein Bromodomain containing 4
DOI: 10.7270/q2sq92gh
title: BindingDB Entry 1364: Compounds and methods for inhibition of AP endonuclease-1/redox factor-1 (HAPE1) activity

description: Small molecule binding data for protein Apurinic-apyrimidinic endonuclease 1 (APE-1), Uniprot P27695
DOI: 10.7270/q2xg9t79
title: BindingDB Entry 1362: Heterocyclic compounds and methods for their use

description: Small molecule binding data for protein Angiotensin II receptor, Uniprot P50052
DOI: 10.7910/dvn/uxz6cu/3g48pt
title: diag_dS_15000_flights.csv

description: :unav
DOI: 10.7910/dvn/uxz6cu/63fuyl
title: diag_R_15000_flights.csv

description: :unav
DOI: 10.7910/dvn/uxz6cu/zox1ba
title: prog_cidx_30000_flights.csv

description: :unav