cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/00002980.0
title: Available IPD datapackage for study 'A Multi-Centre Randomized, Double-Blind Study Comparing Adalimumab (D2E7) Plus Methotrexate With Placebo Plus Methotrexate on Work Disability in Subjects With Early Rheumatoid Arthritis'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5255/ukda-sn-854210
title: Take what you can: Property rights, contestability and conflict 1997-2020
DOI: 10.5519/qd.r5jtpcjp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.14272/cciayanzdwksev-olrukzaisa-n.1
title: C88H54N8Zn2
title: InChI=1S/C88H54N8.2Zn/c1-8-24-55(25-9-1)80-65-41-44-69(90-65)83(58-30-14-4-15-31-58)73-49-52-77(94-73)87(78-53-50-74(95-78)84(59-32-16-5-17-33-59)70-45-42-66(80)91-70)63-39-23-22-38-62(63)64-54-79-85(60-34-18-6-19-35-60)75-47-46-71(92-75)81(56-26-10-2-11-27-56)67-40-43-68(89-67)82(57-28-12-3-13-29-57)72-48-51-76(93-72)86(88(64)96-79)61-36-20-7-21-37-61;;/h1-54H;;/q-4;2*+2/b80-65-,80-66-,81-67-,81-71-,82-68-,82-72-,83-69-,83-73-,84-70-,84-74-,85-75-,85-79-,86-76-,87-77-,87-78-,88-86-;;

description: This dataset contains experimental data from a sample (InChI=1S/C88H54N8.2Zn/c1-8-24-55(25-9-1)80-65-41-44-69(90-65)83(58-30-14-4-15-31-58)73-49-52-77(94-73)87(78-53-50-74(95-78)84(59-32-16-5-17-33-59)70-45-42-66(80)91-70)63-39-23-22-38-62(63)64-54-79-85(60-34-18-6-19-35-60)75-47-46-71(92-75)81(56-26-10-2-11-27-56)67-40-43-68(89-67)82(57-28-12-3-13-29-57)72-48-51-76(93-72)86(88(64)96-79)61-36-20-7-21-37-61;;/h1-54H;;/q-4;2*+2/b80-65-,80-66-,81-67-,81-71-,82-68-,82-72-,83-69-,83-73-,84-70-,84-74-,85-75-,85-79-,86-76-,87-77-,87-78-,88-86-;;). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) mass spectrometry (MS) 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/cciayanzdwksev-olrukzaisa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C88H54N8.2Zn/c1-8-24-55(25-9-1)80-65-41-44-69(90-65)83(58-30-14-4-15-31-58)73-49-52-77(94-73)87(78-53-50-74(95-78)84(59-32-16-5-17-33-59)70-45-42-66(80)91-70)63-39-23-22-38-62(63)64-54-79-85(60-34-18-6-19-35-60)75-47-46-71(92-75)81(56-26-10-2-11-27-56)67-40-43-68(89-67)82(57-28-12-3-13-29-57)72-48-51-76(93-72)86(88(64)96-79)61-36-20-7-21-37-61;;/h1-54H;;/q-4;2*+2/b80-65-,80-66-,81-67-,81-71-,82-68-,82-72-,83-69-,83-73-,84-70-,84-74-,85-75-,85-79-,86-76-,87-77-,87-78-,88-86-;;

description: dataset for mass spectrometry (MS) HRMS (Zn2C88H54N8): calcd 1354.3034, found 1354.3033, 1350.3032 (in intensity of 0.53).
DOI: 10.14272/cciayanzdwksev-olrukzaisa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C88H54N8.2Zn/c1-8-24-55(25-9-1)80-65-41-44-69(90-65)83(58-30-14-4-15-31-58)73-49-52-77(94-73)87(78-53-50-74(95-78)84(59-32-16-5-17-33-59)70-45-42-66(80)91-70)63-39-23-22-38-62(63)64-54-79-85(60-34-18-6-19-35-60)75-47-46-71(92-75)81(56-26-10-2-11-27-56)67-40-43-68(89-67)82(57-28-12-3-13-29-57)72-48-51-76(93-72)86(88(64)96-79)61-36-20-7-21-37-61;;/h1-54H;;/q-4;2*+2/b80-65-,80-66-,81-67-,81-71-,82-68-,82-72-,83-69-,83-73-,84-70-,84-74-,85-75-,85-79-,86-76-,87-77-,87-78-,88-86-;;

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, CD2Cl2, ppm) δ = 9.50 (bs, 1H, Hpyrrole), 8.99 (bs, 1H, Hpyrrole), 8.91 (bs, 1H, Hpyrrole), 8.88 (bs, 1H, Hpyrrole), 8.79 (bs, 1H, Hpyrrole), 8.74 (d, 3J = 4.8 Hz, 1H, Hpyrrole), 8.71 (d, 3J = 4.9 Hz, 1H, Hpyrrole), 8.70 (d, 3J = 4.6 Hz, 1H, Hpyrrole), 8.68 (bs, 1H), 8.64 (d, 3J = 4.6 Hz, 2H, Hpyrrole), 8.58 (bs, 2H), 8.52 (m, 1H), 8.39 (d, 3J = 4.8 Hz, 1H, Hpyrrole), 8.34 (bs, 1H), 8.22–8,14 (m, 2H), 8.14–7.96 (m, 6H), 7.96–7.88 (m, 2H, Haromatic), 7.88–7.83 (m, 3H, Haromatic), 7.83–7.77 (m, 3H, Haromatic), 7.77–7.73 (m, 2H, Haromatic), 7.73–7.68 (m, 4H, Haromatic), 7.68–7.62 (m, 1H, Haromatic), 7.61 (dt, J = 7.6, 1.4 Hz, 1H, Haromatic), 7.56 (m, 4H), 7.52–7.45 (m, 3H), 7.45–7.38 (m, 1H), 7.35 (bs, 1H), 7.08 (bs, 1H), 6.39 (tt, J = 7.7, 1.3 Hz, 1H), 5.37–5.34 (m, 1H), 5.08 (bs, 1H), 4.76 (bs, 1H). Impurities: 4.07, 4.02, 2.61, 2.00, 1.25 (H grease), 0.92–0.79 (H grease), 0.08 (silicon grease) ppm do not belong to the molecule.
DOI: 10.14272/cciayanzdwksev-olrukzaisa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C88H54N8.2Zn/c1-8-24-55(25-9-1)80-65-41-44-69(90-65)83(58-30-14-4-15-31-58)73-49-52-77(94-73)87(78-53-50-74(95-78)84(59-32-16-5-17-33-59)70-45-42-66(80)91-70)63-39-23-22-38-62(63)64-54-79-85(60-34-18-6-19-35-60)75-47-46-71(92-75)81(56-26-10-2-11-27-56)67-40-43-68(89-67)82(57-28-12-3-13-29-57)72-48-51-76(93-72)86(88(64)96-79)61-36-20-7-21-37-61;;/h1-54H;;/q-4;2*+2/b80-65-,80-66-,81-67-,81-71-,82-68-,82-72-,83-69-,83-73-,84-70-,84-74-,85-75-,85-79-,86-76-,87-77-,87-78-,88-86-;;

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2952, 2919, 2850, 1647, 1598, 1458, 1439, 1337, 1259, 1092, 1068, 1001, 992, 795, 742, 730, 720, 700, 671, 660, 650, 569, 527, 432, 401, 394, 377 cm–1.
DOI: 10.5519/qd.zr4px89r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.863o0wws
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.8ffni63r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3755640
title: Microbiota data analysis lesson datasets

description: Datasets to be used in the Carpentry-style lesson on [microbiota data analysis](https://scienceparkstudygroup.github.io/microbiome-lesson/). Datasets from the paper: Zancarini, A., Echenique-Subiabre, I., Debroas, D. <em>et al.</em> Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France. <em>Sci Rep</em> <strong>7, </strong>4344 (2017). https://doi.org/10.1038/s41598-017-04016-w. https://rdcu.be/b3CZh
DOI: 10.5281/zenodo.3755641
title: Microbiota data analysis lesson datasets

description: Datasets to be used in the Carpentry-style lesson on [microbiota data analysis](https://scienceparkstudygroup.github.io/microbiome-lesson/). Datasets from the paper: Zancarini, A., Echenique-Subiabre, I., Debroas, D. <em>et al.</em> Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France. <em>Sci Rep</em> <strong>7, </strong>4344 (2017). https://doi.org/10.1038/s41598-017-04016-w. https://rdcu.be/b3CZh
DOI: 10.5519/qd.o6jnq2z6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.apb492hl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.33dtjjy7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.53vypely
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.sgcz4c6w
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.a4reqjo8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.8bj5x9kw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.9mgjyql6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.m2u1k1l7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3755650
title: BATAL POPS flight (July 2019)

description: The Printed Optical Particle Spectrometer (POPS) was operated during the BATAL (Balloon Measurements of the Asian Tropopause Aerosol Layer) campaign in 2019 in Hyderabad (17N,78E), India. The primary goal of this campaign is the investigation of the ATAL (Asian Tropopause Aerosol Layer). Here, we use POPS observations from one balloon flight on July 17th 2019 (UT time). The instrument weighs around 800g and uses a 405 mm diode laser. POPS delivers aerosol number concentration and size distribution measurements. Here the file corresponds to concentrations in each size class between 0.15 and ~3 µm.<br>
DOI: 10.5281/zenodo.3755651
title: BATAL POPS flight (July 2019)

description: The Printed Optical Particle Spectrometer (POPS) was operated during the BATAL (Balloon Measurements of the Asian Tropopause Aerosol Layer) campaign in 2019 in Hyderabad (17N,78E), India. The primary goal of this campaign is the investigation of the ATAL (Asian Tropopause Aerosol Layer). Here, we use POPS observations from one balloon flight on July 17th 2019 (UT time). The instrument weighs around 800g and uses a 405 mm diode laser. POPS delivers aerosol number concentration and size distribution measurements. Here the file corresponds to concentrations in each size class between 0.15 and ~3 µm.<br>
DOI: 10.5519/qd.aca8ralm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.jzoj3ktm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3755244
title: SignalTrain Concatenated Dataset

description: Dataset used in "SignalTrain Continued: Modeling Time-Dependent Nonlinear Signal Processing Effects Using Deep Neural Networks"
DOI: 10.5281/zenodo.3755245
title: SignalTrain Concatenated Dataset

description: Dataset used in "SignalTrain Continued: Modeling Time-Dependent Nonlinear Signal Processing Effects Using Deep Neural Networks"
DOI: 10.5519/qd.ki5mzwwg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.s000e7qi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (82944 records)
DOI: 10.21227/50mq-q415
title: UP - Fall Detection

description: [17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets. Furthermore, this dataset is associated to a published article. The latter presents main results for fall detection that can be used as baseline.
DOI: 10.5519/qd.tvkp0m70
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.h8kc3o25
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5255/ukda-sn-854139
title: Long‐run persistence of trust and corruption in the bureaucracy 2010-2015
DOI: 10.21227/j11y-jg08
title: 21st Century Bordeaux Wines Dataset

description: Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.
DOI: 10.21227/n6rx-f997
title: 21st Century Bordeaux Wines Dataset

description: Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.
DOI: 10.5519/qd.i6qybmzz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.r6zmd5xf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.xpwlzbvq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3755652
title: Water Supply Networks

description: This file contains five water supply networks in INP format.
DOI: 10.5281/zenodo.3755653
title: Water Supply Networks

description: This file contains five water supply networks in INP format.
DOI: 10.5519/qd.audvx5em
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (89023 records)
DOI: 10.5519/qd.bmpcs18b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.kdb40gal
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25345/c5fq3x
title: MassIVE MSV000085302 - GNPS_Cylindrocarpons Sp with chemical elicitors.
title: The endophytic fungi Cylindrocarpon sp is co-cultivation with different salts (NaCl, NaBr and KNO3).
DOI: 10.5281/zenodo.3755984
title: Deep sea dissolved organic nitrogen and phosphorus (DON and DOP) at Station ALOHA in the North Pacific Subtropical Gyre

description: Data on organic and inorganic nutrients in unfiltered seawater that was sampled at and around Station ALOHA, north of Oahu, Hawaii, in the North Pacific Subtropical Gyre. Seawater was collected into HDPE or polypropylene bottles and immediately frozen. Silicate, phosphate and nitrate+nitrite are determined colormetrically on a SEAL Analytical Autoanalyzer (AA3 with HR detectors), with the exception of nitrate+nitrite that is &lt;0.5umol/L, which is analyzed by high-sensitivity chemiluminescence. Total phosphorus (TP) and total nitrogen (TN) are determined by analysis of phosphate and nitrate, respectively, after oxidation by high-intensity ultraviolet light. Total organic phosphorus and total organic nitrogen are determined by subtracting background PO4 and NO3+NO2 from TP and TN, respectively. Total organic carbon is determined by combustion on a Shimadzu TOC-V analyzer. This dataset was originally published in the following article, in which additional details and interpretations of the data can be found: R. K. Foreman, K. M. Björkman, C. A. Carlson, K. Opalk, D. M. Karl, (2019). Improved ultraviolet photo‐oxidation system yields estimates for deep‐sea dissolved organic nitrogen and phosphorus, Limnol. Oceanogr. Methods, doi.org/10.1002/lom3.10312 <br>
description: {"references": ["R. K. Foreman, K. M. Bj\u00f6rkman, C. A. Carlson, K. Opalk, D. M. Karl, (2019). Improved ultraviolet photo\u2010oxidation system yields estimates for deep\u2010sea dissolved organic nitrogen and phosphorus, \u00a0Limnol. Oceanogr. Methods,\u00a0doi.org/10.1002/lom3.10312"]}
DOI: 10.5281/zenodo.3755985
title: Deep sea dissolved organic nitrogen and phosphorus (DON and DOP) at Station ALOHA in the North Pacific Subtropical Gyre

description: Data on organic and inorganic nutrients in unfiltered seawater that was sampled at and around Station ALOHA, north of Oahu, Hawaii, in the North Pacific Subtropical Gyre. Seawater was collected into HDPE or polypropylene bottles and immediately frozen. Silicate, phosphate and nitrate+nitrite are determined colormetrically on a SEAL Analytical Autoanalyzer (AA3 with HR detectors), with the exception of nitrate+nitrite that is &lt;0.5umol/L, which is analyzed by high-sensitivity chemiluminescence. Total phosphorus (TP) and total nitrogen (TN) are determined by analysis of phosphate and nitrate, respectively, after oxidation by high-intensity ultraviolet light. Total organic phosphorus and total organic nitrogen are determined by subtracting background PO4 and NO3+NO2 from TP and TN, respectively. Total organic carbon is determined by combustion on a Shimadzu TOC-V analyzer. This dataset was originally published in the following article, in which additional details and interpretations of the data can be found: R. K. Foreman, K. M. Björkman, C. A. Carlson, K. Opalk, D. M. Karl, (2019). Improved ultraviolet photo‐oxidation system yields estimates for deep‐sea dissolved organic nitrogen and phosphorus, Limnol. Oceanogr. Methods, doi.org/10.1002/lom3.10312 <br>
description: {"references": ["R. K. Foreman, K. M. Bj\u00f6rkman, C. A. Carlson, K. Opalk, D. M. Karl, (2019). Improved ultraviolet photo\u2010oxidation system yields estimates for deep\u2010sea dissolved organic nitrogen and phosphorus, \u00a0Limnol. Oceanogr. Methods,\u00a0doi.org/10.1002/lom3.10312"]}
DOI: 10.5519/qd.4jd1emoe
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.iufr5wnw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.ytw127in
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.cgplnd22
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.0xi78y7i
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.29roxoqx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.mexai2gd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.qh29opvi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.dx6z572e
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.m3otnafr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.oxejb1ei
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.zuin4kb2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25739/anqq-sg86
title: Genomes to Fields 2018 Data Set

description: The dataset contains results from phenotypic evaluations of multi location field trials and genotypic evaluation using practical haplotype graphs of multiple corn populations. Also included is field level weather and soil data for the multiple locations where field testing were conducted. Data was collected by collaborators in the Genomes to Fields (G2F) initiative. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.
DOI: 10.15468/5mszwt
title: Mosquitoes (Diptera: Culicidae) originally described from French Guiana

description: This dataset contains the digitized treatments in Plazi based on the original journal article Talaga, Stanislas, Gantier, Jean-Charles, Girod, Romain (2020): Mosquitoes (Diptera: Culicidae) originally described from French Guiana. Zootaxa 4747 (2): 361-377, DOI: 10.11646/zootaxa.4747.2.8
DOI: 10.5281/zenodo.3755732
title: METAGENÓMICA

description: Datos sobre análisis metagenómicos.
DOI: 10.5281/zenodo.3755733
title: METAGENÓMICA

description: Datos sobre análisis metagenómicos.
DOI: 10.5281/zenodo.3755451
title: Transfer Learning for leveraging computer vision in infrastructure maintenance [extracted features]

description: Dataset containing features extracted from images taken on single case of infrastructure facility for the purpose of training Transfer Learned CNN classifier. It is meant to be used with KrakN framework (https://github.com/MatZar01/KrakN), published with the research paper.
DOI: 10.5281/zenodo.3755452
title: Transfer Learning for leveraging computer vision in infrastructure maintenance [extracted features]

description: Dataset containing features extracted from images taken on single case of infrastructure facility for the purpose of training Transfer Learned CNN classifier. It is meant to be used with KrakN framework (https://github.com/MatZar01/KrakN), published with the research paper.
DOI: 10.21227/hv6y-x348
title: 21st Century Bordeaux Wines Dataset

description: Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.
DOI: 10.6084/m9.figshare.12144141
title: Additional file 1 of Genetic and metabolic links between the murine microbiome and memory

description: Additional file 1.
DOI: 10.6084/m9.figshare.12144141.v1
title: Additional file 1 of Genetic and metabolic links between the murine microbiome and memory

description: Additional file 1.