cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n.3
title: 5,15-Dibromo-10,20-bis(4-ethoxycarbonylphenyl)porphyrin
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: This dataset contains experimental data from a sample (InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000593.1
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 9.62 (d, J = 4.9 Hz, 4H, Hpyrrole), 8.78 (d, J = 4.8 Hz, 4H, Hpyrrole), 8.47 (d, J = 8.1 Hz, 4H, Haromatic), 8.23 (d, J = 8.1 Hz, 4H, Haromatic), 4.60 (q, J = 7.1 Hz, 4H, CH2), 1.57 (t, J = 7.1 Hz, 6H, CH3), ,-2.77 (s, 2H, NH). Impurtities: 10.35 (unknown), 5.30 (CH2Cl2), 1.26 (grease), 0.93–0.80 (grease), 0.08 (grease). In accordance with DOI: 10.14272/DJGWMTKKMOZBMF-YVBDPAHPSA-N.1
DOI: 10.14272/qkhpypucyczmqu-wtfxtoscsa-n/chmo0000593.1
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C38H30N4O4/c1-3-45-37(43)25-9-5-23(6-10-25)35-31-17-13-27(39-31)21-29-15-19-33(41-29)36(24-7-11-26(12-8-24)38(44)46-4-2)34-20-16-30(42-34)22-28-14-18-32(35)40-28/h5-22,39,42H,3-4H2,1-2H3/b27-21-,28-22-,29-21-,30-22-,35-31-,35-32-,36-33-,36-34-

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 10.36 (s, 2H, Hmeso), 9.43 (d, J = 4.6 Hz, 4H, Hpyrrole), 9.05 (d, J = 4.6 Hz, 4H, Hpyrrole), 8.51 (d, J = 8.1 Hz, 4H, Haromatic), 8.37 (d, J = 8.1 Hz, 4H, Haromatic), 4.63 (q, J = 7.2 Hz, 4H, COCH2), 1.58 (t, J = 7.1 Hz, 6H, CH2CH3), -3.12 (br, s, 2H, NH). In accordance with DOI: 10.14272/QKHPYPUCYCZMQU-WTFXTOSCSA-N.1
DOI: 10.14272/qkhpypucyczmqu-wtfxtoscsa-n.2
title: C38H30N4O4
title: InChI=1S/C38H30N4O4/c1-3-45-37(43)25-9-5-23(6-10-25)35-31-17-13-27(39-31)21-29-15-19-33(41-29)36(24-7-11-26(12-8-24)38(44)46-4-2)34-20-16-30(42-34)22-28-14-18-32(35)40-28/h5-22,39,42H,3-4H2,1-2H3/b27-21-,28-22-,29-21-,30-22-,35-31-,35-32-,36-33-,36-34-

description: This dataset contains experimental data from a sample (InChI=1S/C38H30N4O4/c1-3-45-37(43)25-9-5-23(6-10-25)35-31-17-13-27(39-31)21-29-15-19-33(41-29)36(24-7-11-26(12-8-24)38(44)46-4-2)34-20-16-30(42-34)22-28-14-18-32(35)40-28/h5-22,39,42H,3-4H2,1-2H3/b27-21-,28-22-,29-21-,30-22-,35-31-,35-32-,36-33-,36-34-). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol)
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol)
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3, ppm) δ = 156.0, 151.2, 134.0, 133.8, 131.1, 129.8, 129.4, 129.1, 128.2, 128.1, 127.3, 126.4, 124.9, 124.8, 124.2, 123.2, 117.5, 115.3, 115.0, 113.8, 56.7. Impurities: 29.9 (grease), 24.0 (unknown).
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol)
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3489, 3454, 3451, 3437, 3425, 3412, 3398, 3390, 3377, 3360, 3327, 3054, 3004, 2959, 2934, 2837, 1618, 1589, 1504, 1460, 1431, 1378, 1361, 1344, 1330, 1302, 1261, 1247, 1204, 1171, 1143, 1128, 1081, 1052, 1018, 970, 938, 902, 863, 809, 775, 745, 708, 677, 664, 623, 596, 574, 533, 521, 493, 459, 435, 422, 387, 378 cm–1.
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol)
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: dataset for mass spectrometry (MS) MS (FAB, 3-NBA), m/z (%): 301 (47) [M+H]+, 300 (100) [M]+, 158 (10) [M-C10H6O]+, 154 (21), 149 (23), 137 (12), 136 (13).HRMS–FAB (m/z): [C21H16O2]+ calcd 300.1150; found 300.1143.
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n.1
title: 1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: This dataset contains experimental data from a sample (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol, InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) mass spectrometry (MS) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/syssvmyyshymaq-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1-(2-methoxynaphthalen-1-yl)naphthalen-2-ol)
title: InChI=1S/C21H16O2/c1-23-19-13-11-15-7-3-5-9-17(15)21(19)20-16-8-4-2-6-14(16)10-12-18(20)22/h2-13,22H,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 8.06 (d, J = 9.0 Hz, 1H, Haromatic), 7.93–7.88 (m, 2H, Haromatic), 7.88–7.83 (m, 1H, Haromatic), 7.49 (d, J = 9.1 Hz, 1H, Haromatic), 7.42–7.36 (m, 1H, Haromatic), 7.36–7.33 (m, 1H, Haromatic), 7.32–7.30 (m, 1H, Haromatic), 7.30–7.26 (m, 1H, Haromatic), 7.22 (ddd, J = 8.2, 6.8, 1.4 Hz, 1H, Haromatic), 7.18 (dd, J = 8.6, 1.1 Hz, 1H, Haromatic), 7.05 (dd, J = 8.6, 1.1 Hz, 1H, Haromatic), 4.92 (s, 1H, OH), 3.81 (s, 3H, OCH3) ppm. Impurities: 1.55 (water), 1.26 (grease), 1.24 (s), 1.22(s), 0.96 – 0.86 (m, grease), 0.08 (s, grease).
DOI: 10.14272/pbtprehatafben-uhfffaoysa-n.2
title: 2-(1H-pyrrol-2-ylmethyl)-1H-pyrrole
title: InChI=1S/C9H10N2/c1-3-8(10-5-1)7-9-4-2-6-11-9/h1-6,10-11H,7H2

description: This dataset contains experimental data from a sample (2-(1H-pyrrol-2-ylmethyl)-1H-pyrrole, InChI=1S/C9H10N2/c1-3-8(10-5-1)7-9-4-2-6-11-9/h1-6,10-11H,7H2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/pbtprehatafben-uhfffaoysa-n/chmo0000593.1
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-(1H-pyrrol-2-ylmethyl)-1H-pyrrole)
title: InChI=1S/C9H10N2/c1-3-8(10-5-1)7-9-4-2-6-11-9/h1-6,10-11H,7H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 7.91 (br, 2H, NH), 6.62 (pseudo-td, J = 2.7, 1.6 Hz, 2H, Hpyrrole), 6.18 (pseudo-q, J = 2.9 Hz, 2H, Hpyrrole), 6.03–6.09 (m, 2H, Hpyrrole), 3.94 (s, 2H, CH2). In accordance with DOI: 10.14272/PBTPREHATAFBEN-UHFFFAOYSA-N.1
DOI: 10.5281/zenodo.6554002
title: french population data

description: Cleanup of french city's population dataset : https://www.insee.fr/fr/statistiques/3698339
DOI: 10.5281/zenodo.6554001
title: french population data

description: Cleanup of french city's population dataset : https://www.insee.fr/fr/statistiques/3698339
DOI: 10.5525/gla.researchdata.1289
title: I-Rheo

description: I-Rheo is a MATLAB app designed to evaluate the Complex Modulus and the Complex Viscosity by means of the Fourier transform of the time-dependent stress and strain.
DOI: 10.17034/fee228e0-c0cf-4a3b-af8a-7668284c5f4e
title: Pork consumers and their preferences for purchasing "raised without antibiotics" pork

description: The tabular, quantitative dataset provides information on consumers and their intention to buy “raised without antibiotics” labelled pork and hypothetical QR code labelled pork products. It also provides information relating to consumers’ knowledge and awareness of antimicrobial use and resistance (AMR). The dataset is a representative sample of the UK. The sample (n=1000) was quota-controlled in terms of age, sex, socio-economic status, and region to match the known demographics of the population. Sociodemographic factors such as marital status, education, occupation status, household income, and number of children and adults in household are presented. This dataset relates to research in pork, consumer perception, food labelling, and consumer purchase intention, and would be of use to researchers investigating these fields. A codebook is also provided presenting a key to the dataset. Both dataset and codebook are supplied in xlsx and CSV formats.
DOI: 10.5519/qd.kqyqeehw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.6554043
title: KUALA: A Machine Learning-driven framework for kinase inhibitors repositioning

description: The complete list of all predicted compounds for each kinase accompanied by RT thresholds.
DOI: 10.5281/zenodo.6554042
title: KUALA: A Machine Learning-driven framework for kinase inhibitors repositioning

description: The complete list of all predicted compounds for each kinase accompanied by RT thresholds.
DOI: 10.6084/m9.figshare.19773070
title: Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods

description: SNP datasets for the association panel.
DOI: 10.20387/bonares-z2ak-s160
title: Federal state-scale soil management-related stakeholder database in German agricultural sector

description: In order to support sustainable soil management, an exchange between scientists and other soil-related stakeholders is crucial. Previously, an overview on the soil-related stakeholders in Germany was published at the national level (https://doi.org/10.13039/501100002347). This database is further overview of the soil-related stakeholders, though, at the German federal state level (in German). 
The three listed-below stakeholder groups, which were assumed to have a direct or indirect influence on soil functions, were initially derived from the drivers of soil management (i.e. socio‐economic, biophysical, and technology). The groups were defined such as (1) state authorities, (2) civil societies, and (3) agricultural associations. The stakeholders’ influence/power and interest were evaluated with the focus of organizations on the five main soil functions, i.e. production, water purification, carbon sequestration, biodiversity, recycling of nutrients and chemicals. 
The level of the influence/power and interest was estimated in a relative matter among the stakeholders by taking into account such parameters as the number of members/partners/employees, the visibility of organizations (via projects, products, policies, press, publications etc.) as well as the hierarchy among the state organizations. The level of awareness of stakeholders’ activities was also used in assessing the stakeholders’ influence.

Research domain: Soil Sciences

Research question: The aim was to create a stakeholder database, representing soil-related stakeholders across Germany at the federal state scale.
DOI: 10.6084/m9.figshare.19169609
title: Data and code from Semchenko et al. (2022) New Phytologist 10.1111/nph.18118

description: Data and code used in Semchenko M, Barry KE, de Vries FT, Mommer L, Maciá-Vicente JG (2022) Deciphering the role of specialist and generalist plant–microbial interactions as drivers of plant–soil feedback. New Phytologist, 10.1111/nph.18118 for the analysis of host ranges across plant-associated fungal and oomycete guilds. <br>
DOI: 10.6084/m9.figshare.19169609.v1
title: Data and code from Semchenko et al. (2022) New Phytologist 10.1111/nph.18118

description: Data and code used in Semchenko M, Barry KE, de Vries FT, Mommer L, Maciá-Vicente JG (2022) Deciphering the role of specialist and generalist plant–microbial interactions as drivers of plant–soil feedback. New Phytologist, 10.1111/nph.18118 for the analysis of host ranges across plant-associated fungal and oomycete guilds. <br>
DOI: 10.5281/zenodo.6554191
title: Economic damages for the UK

description: Regional estimates of economic damages across the UK by sector, for sectors driven by local climate.
DOI: 10.18419/darus-1847/1
title: Chan_vese.m

description: This file contains the workflow of Chan-Vese method
DOI: 10.18419/darus-1847/2
title: Frac_Unet_training.py

description: With the pre-processed training data, the code performs training of U-net model and saves a trained model after training.
DOI: 10.18419/darus-1847/3
title: Local_threshold.py

description: This file contains the workflow of the local threshold method
DOI: 10.18419/darus-1847/4
title: img_seg.py

description: This file contains the load/save functions and core functions of the Sato and the Local threshold methods.
DOI: 10.18419/darus-1847/5
title: unet_v2_mod.py

description: The code to create the U-net architecture
DOI: 10.18419/darus-1847/6
title: Sato.py

description: This file contains the workflow of the Sato method
DOI: 10.18419/darus-1847/7
title: Frac_Unet_preprocess.py

description: Preparation of training data can be done with the code
DOI: 10.18419/darus-1847/8
title: classifier.model

description: This is the trained model with the Random forest method. This file could be loaded in the open source program ImageJ with TrainableWeka library. The predictions can be generated with processing.bsh code.
DOI: 10.18419/darus-1847/9
title: processing.bsh

description: Prediction generator of the random forest method. The input file path, save path and trained model have to be chosen.
DOI: 10.18419/darus-1847/10
title: trained_model_U_net_27.hdf5

description: Trained model of the U-net model. This file is required to reproduce the results.
DOI: 10.13020/kswg-6d07
title: Changes in protein kinase A conformational entropy by ATP-competitive inhibitors
DOI: 10.18419/darus-1847/11
title: post_random_forest.py

description: The predictions of the Random forest method can be merged into the original size of image with the code.
DOI: 10.17030/uclan.data.00000334
title: Wellbeing in schools: what role does recognition play? Phase 2: Qualitative data collection
DOI: 10.18419/darus-1847/12
title: readme.txt

description: This file contains instruction of how to operate the codes.
DOI: 10.18419/darus-1847/13
title: Frac_Unet_postprocess.py

description: With the code, the predictions of trained model for given input files can be generated and saved.
DOI: 10.5281/zenodo.6554110
title: How to charge while drive: Scheduling point-to-point deliveries of an electric vehicle under overhead wiring

description: Test data used in Section 5.2 |J|_|R|_L_l_h.txt |J|: number of jobs<br> |R|: number of ramps<br> L: highway length<br> l: share of electrification<br> h: instance |J|;|R|;l; |J|: number of jobs<br> |R|: number of ramps<br> l: share of electrification Truck (C;c0;rd;r0;delta+;delta-;) C: maximum charge level<br> c0: initial charge level<br> rd: final position<br> r0: initial position<br> delta+: charging rate<br> delta-: consumption rate Path (p0=0; p1; ...; p|R|=L;) pr: position of ramp r Electrified (#;first elec?; sect): #: number of sections<br> elec?: true =&gt; first section electrified (alternating)<br> false =&gt; first section NOT electrified (alternating)<br> sect: list of sections<br> (Note: two directions! 0-L and L-0) Jobs (j;lo/delta-;ld/delta-;rd;ro) j: Index<br> lo/delta-: (Total travel distance beyond the highway for picking up)/consumption rate<br> ld/delta-: (Total travel distance beyond the highway for delivering)/consumption rate<br> rd: Ramp where the ECV leaves the highway to deliver<br> ro: Ramp where the ECV leaves the highway to pick up Jobs (ldo): j: Index<br> j': Index<br> ldo: Total travel distance beyond the highway for delivering job j and picking up j' with rdj = roj'<br> (&gt;C: exceeds capacity)
DOI: 10.5281/zenodo.6554109
title: How to charge while drive: Scheduling point-to-point deliveries of an electric vehicle under overhead wiring

description: Test data used in Section 5.2 |J|_|R|_L_l_h.txt |J|: number of jobs<br> |R|: number of ramps<br> L: highway length<br> l: share of electrification<br> h: instance |J|;|R|;l; |J|: number of jobs<br> |R|: number of ramps<br> l: share of electrification Truck (C;c0;rd;r0;delta+;delta-;) C: maximum charge level<br> c0: initial charge level<br> rd: final position<br> r0: initial position<br> delta+: charging rate<br> delta-: consumption rate Path (p0=0; p1; ...; p|R|=L;) pr: position of ramp r Electrified (#;first elec?; sect): #: number of sections<br> elec?: true =&gt; first section electrified (alternating)<br> false =&gt; first section NOT electrified (alternating)<br> sect: list of sections<br> (Note: two directions! 0-L and L-0) Jobs (j;lo/delta-;ld/delta-;rd;ro) j: Index<br> lo/delta-: (Total travel distance beyond the highway for picking up)/consumption rate<br> ld/delta-: (Total travel distance beyond the highway for delivering)/consumption rate<br> rd: Ramp where the ECV leaves the highway to deliver<br> ro: Ramp where the ECV leaves the highway to pick up Jobs (ldo): j: Index<br> j': Index<br> ldo: Total travel distance beyond the highway for delivering job j and picking up j' with rdj = roj'<br> (&gt;C: exceeds capacity)
DOI: 10.25422/azu.data.16638328
title: Corpus of Contemporary American English (COCA) 1990 to 2012 Datasets

description: <strong>Dataset available only to University of Arizona affiliates. To obtain access, you must log into ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.</strong> <strong>In no case can substantial amounts of the full-text data (typically a total of 50,000 words or more) be distributed outside the University of Arizona. If portions of the derived data are made available to others, it cannot include substantial portions of the raw frequency of words. Any publications of products that are based on the data should contain a reference to the source of the data: http://corpus.byu.edu/full-text/. COCA cannot be used to create software or products that will be sold to others.</strong> This database is only available on the COCA website. To access the data, follow the link provided (https://coca.library.arizona.edu). The Corpus of Contemporary American English (COCA) is the largest freely-available corpus of English, and the only large and balanced corpus of American English. The corpus was created by Mark Davies of Brigham Young University, and it is used by tens of thousands of users every month (linguists, teachers, translators, and other researchers). COCA is also related to other large corpora that we have created. The corpus contains more than 450 million words of text and is equally divided among spoken, fiction, popular magazines, newspapers, and academic texts. It includes 20 million words each year from 1990-2012 and the corpus is also updated regularly (the most recent texts are from Summer 2012). Because of its design, it is perhaps the only corpus of English that is suitable for looking at current, ongoing changes in the language. <em></em> <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.25422/azu.data.16638328.v2
title: Corpus of Contemporary American English (COCA) 1990 to 2012 Datasets

description: <strong>Dataset available only to University of Arizona affiliates. To obtain access, you must log into ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.</strong> <strong>In no case can substantial amounts of the full-text data (typically a total of 50,000 words or more) be distributed outside the University of Arizona. If portions of the derived data are made available to others, it cannot include substantial portions of the raw frequency of words. Any publications of products that are based on the data should contain a reference to the source of the data: http://corpus.byu.edu/full-text/. COCA cannot be used to create software or products that will be sold to others.</strong> This database is only available on the COCA website. To access the data, follow the link provided (https://coca.library.arizona.edu). The Corpus of Contemporary American English (COCA) is the largest freely-available corpus of English, and the only large and balanced corpus of American English. The corpus was created by Mark Davies of Brigham Young University, and it is used by tens of thousands of users every month (linguists, teachers, translators, and other researchers). COCA is also related to other large corpora that we have created. The corpus contains more than 450 million words of text and is equally divided among spoken, fiction, popular magazines, newspapers, and academic texts. It includes 20 million words each year from 1990-2012 and the corpus is also updated regularly (the most recent texts are from Summer 2012). Because of its design, it is perhaps the only corpus of English that is suitable for looking at current, ongoing changes in the language. <em></em> <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>