cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4042170
title: Duke PAM Dataset

description: Duke PAM is a photoacoustic microscopy (PAM) dataset collected at Duke University with the optical resolution PAM system described in (M. Chen et al., "Simultaneous photoacoustic imaging of intravascular and tissue oxygenation," <em>Optics Letters</em>, vol. 44, no. 15, pp. 3773-3776, 2019.) at a wavelength of 532 nm. This dataset is composed of primarily mouse brain microvasculature images, with a few images of mouse ear and tumors. Although collected in 3-D, for the purposes of viewing, collection, and storage, the images have been projected into 2-D using maximum amplitude projection (MAP). These data were collected with support from the National Institutes of Health (R01 EB028143, R01 NS111039, R01 NS115581, R21 EB027304, R43 CA243822, R43 CA239830, R44 HL138185); Duke MEDx Basic Science Grant; Duke Center for Genomic and Computational Biology Faculty Research Grant; Duke Institute of Brain Science Incubator Award; American Heart Association Collaborative Sciences Award (18CSA34080277). The "clean" subset contains PAM images of variable size that have been preprocessed and stored as uint8 .jpg images. The "patches" subset contains non-overlapping 128-by-128 pixel patches of the "clean" images stored as uint8 .jpg images. The "raw" subset contains PAM images of variable size before they have been preprocessed (stored as uint16 .png images). For general deep learning tasks, the "clean" dataset can be used with random crop (none of the images have dimensions less than 128-by-128). If uniformly-sized images are desired without needing to use random crop, the "patches" subset would work best. The "raw" subset works best for deep learning tasks that expect input images that are closer to raw/unprocessed PAM images and are willing to take on the memory burden of loading uint16 .png images.
DOI: 10.25921/qnwh-6p11
title: EM122 Water Column Sonar Data Collected During RB10TR
DOI: 10.25921/csj9-zd02
title: EK60 Water Column Sonar Data Collected During NF-14-08
DOI: 10.25921/1h87-xf50
title: EK60 Water Column Sonar Data Collected During NF-14-07
DOI: 10.25921/5kth-fz07
title: EK60 Water Column Sonar Data Collected During NF-13-06L1
DOI: 10.15468/j8hcuz
title: The Upper Devonian fish Bothriolepis (Placodermi: Antiarchi) from near Canowindra, New South Wales, Australia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Johanson, Zerina (1998): The Upper Devonian fish Bothriolepis (Placodermi: Antiarchi) from near Canowindra, New South Wales, Australia. Records of the Australian Museum 50 (3): 315-348, DOI: 10.3853/j.0067-1975.50.1998.1289, URL: https://journals.australian.museum/johanson-1998-rec-aust-mus-503-315348/
DOI: 10.25921/y79d-e098
title: EM302 Water Column Sonar Data Collected During FB180001
DOI: 10.18430/m3.irrmc.5869
title: X-ray diffraction data for the IDP95820_7mbl project
DOI: 10.25921/9vzc-zc70
title: EM2040 Water Column Sonar Data Collected During KRY13_04
DOI: 10.25921/g7b2-cx69
title: EM2040 Water Column Sonar Data Collected During KRY13_03
DOI: 10.25921/s6wz-3707
title: EK60 Water Column Sonar Data Collected During NF-14-04
DOI: 10.25921/ge90-8k88
title: EM2040 Water Column Sonar Data Collected During CV14_01
DOI: 10.25921/7xgw-pz46
title: EM2040 Water Column Sonar Data Collected During CV14_02
DOI: 10.25921/vtx0-4x14
title: EK60 Water Column Sonar Data Collected During NF-13-06L2
DOI: 10.25921/4t16-sm59
title: EM2040 Water Column Sonar Data Collected During CV14_03
DOI: 10.25921/47sp-rs59
title: EM122 Water Column Sonar Data Collected During RB1201
DOI: 10.25921/w6th-ba50
title: EM122 Water Column Sonar Data Collected During RB1008
DOI: 10.25921/y3ty-e274
title: EK60 Water Column Sonar Data Collected During CE16014
DOI: 10.15468/8f6q4u
title: A revision of the genus Minibiotus (Tardigrada: Macrobiotidae) with descriptions of eleven new species from Australia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Claxton, Sandra K. (1998): A revision of the genus Minibiotus (Tardigrada: Macrobiotidae) with descriptions of eleven new species from Australia. Records of the Australian Museum 50 (2): 125-160, DOI: 10.3853/j.0067-1975.50.1998.1276, URL: https://journals.australian.museum/claxton-1998-rec-aust-mus-502-125160/
DOI: 10.25921/5w2n-7b87
title: EM3002 Water Column Sonar Data Collected During CV13_01
DOI: 10.25921/heyd-jb29
title: EK60 Water Column Sonar Data Collected During NF-14-03
DOI: 10.25921/5e48-n078
title: EM302 Water Column Sonar Data Collected During CE16007
DOI: 10.25921/6g3q-jj29
title: EK60 Water Column Sonar Data Collected During HB1903
DOI: 10.6084/m9.figshare.12815201.v1
title: Data for Experiments with Interactive Fault Localization Using Simulated and Real Users

description: Data used for the experiments in the paper.
DOI: 10.25921/etja-3n73
title: EM122 Water Column Sonar Data Collected During RB1103
DOI: 10.25921/djnz-b340
title: EM122 Water Column Sonar Data Collected During RB-18-03
DOI: 10.25921/fhnc-cc18
title: EM122 Water Column Sonar Data Collected During RB-18-01
DOI: 10.25921/vy6y-eh94
title: EM122 Water Column Sonar Data Collected During RB-18-05
DOI: 10.25921/rh5g-mb26
title: EK80 Water Column Sonar Data Collected on DP06
DOI: 10.25921/jr05-c237
title: EM2040 Water Column Sonar Data Collected During H13048
DOI: 10.25921/300a-qp05
title: EM2040 Water Column Sonar Data Collected During H13177
DOI: 10.25921/5qpy-5412
title: EM122 Water Column Sonar Data Collected During FSP180002
DOI: 10.25921/de0r-hb94
title: EK500 Water Column Sonar Data Collected During DS0204
DOI: 10.25921/yb3n-mw74
title: EK500 Water Column Sonar Data Collected During DS0404
DOI: 10.25921/tzx1-zc28
title: EK60 Water Column Sonar Data Collected During DS0604
DOI: 10.25921/xtb8-th47
title: EK60 Water Column Sonar Data Collected During DS0903
DOI: 10.25921/y2rj-0g29
title: EK60 Water Column Sonar Data Collected During DY0604
DOI: 10.25921/6pcm-gw85
title: ES60 Water Column Sonar Data Collected During FR0307
DOI: 10.25921/1kc8-6090
title: EK60 Water Column Sonar Data Collected During NH1010
DOI: 10.25921/drxs-hr18
title: EK60 Water Column Sonar Data Collected During OS1209
DOI: 10.25921/djdh-3h80
title: EK60 Water Column Sonar Data Collected During PC1007
DOI: 10.25921/8tqk-ya73
title: ME70 Water Column Sonar Data Collected During PC1007
DOI: 10.25921/6dbv-6x16
title: EK60 Water Column Sonar Data Collected During RL1601
DOI: 10.25921/vyan-d363
title: EK80 Water Column Sonar Data Collected During RL1601
DOI: 10.25921/vshb-bs46
title: EK500 Water Column Sonar Data Collected During RR0304
DOI: 10.25921/h0dj-a809
title: EK60 Water Column Sonar Data Collected During SH1203
DOI: 10.25921/e05h-z833
title: ME70 Water Column Sonar Data Collected During SH1203
DOI: 10.25318/3310030701-eng
title: Business or organization expectations over the next three months, by business characteristics

description: Expectations over the next three months by the business or organization, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25921/fs3m-vx92
title: ME70 Water Column Sonar Data Collected During SH1306
DOI: 10.25921/0j1x-tb70
title: EK60 Water Column Sonar Data Collected During SH1401
DOI: 10.25921/dd0e-m992
title: EK60 Water Column Sonar Data Collected During SH1604
DOI: 10.25921/ph7z-xr87
title: EM302 Water Column Sonar Data Collected During FS180002
DOI: 10.25921/neh6-4z23
title: EM2040 Water Column Sonar Data Collected During F00783
DOI: 10.25921/2ax6-r063
title: EK60 Water Column Sonar Data Collected During HB1901
DOI: 10.25921/pnqz-hb33
title: EK60 Water Column Sonar Data Collected During HB1902
DOI: 10.25921/ym08-yy17
title: EK60 Water Column Sonar Data Collected During HB1904
DOI: 10.25921/k6fd-4461
title: ME70 Water Column Sonar Data Collected During HB1904
DOI: 10.25921/vt45-sa66
title: EK60 Water Column Sonar Data Collected During HB1906
DOI: 10.25921/5fwr-0r29
title: EK60 Water Column Sonar Data Collected During HB1907
DOI: 10.25921/8pby-kz66
title: EM122 Water Column Sonar Data Collected During FD170001
DOI: 10.25921/3ff3-ks17
title: EM302 Water Column Sonar Data Collected During CE16014
DOI: 10.25921/71ms-ag24
title: EM302 Water Column Sonar Data Collected During FA150002
DOI: 10.25921/2jgb-9429
title: EM302 Water Column Sonar Data Collected During FA160003
DOI: 10.25921/cc58-5t19
title: EM710 Water Column Sonar Data Collected During W00480
DOI: 10.25921/4ec0-eh93
title: EM710 Water Column Sonar Data Collected During W00476
DOI: 10.25921/cpfv-a003
title: EK500 Water Column Sonar Data Collected During DS0004
DOI: 10.25921/xv0s-qr52
title: EK500 Water Column Sonar Data Collected During DS0801
DOI: 10.25921/7fb3-sx13
title: EK60 Water Column Sonar Data Collected During NH1001
DOI: 10.25921/sp5c-tf02
title: EK60 Water Column Sonar Data Collected During NH1201
DOI: 10.25318/3310030701-fra
title: Attentes des entreprises ou des organismes pour les trois prochains mois, selon les caractéristiques de l’entreprise

description: Attentes des entreprises ou des organismes pour les trois prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité commerciale et le propriétaire majoritaire.
DOI: 10.25921/9jae-4t63
title: EM302 Water Column Sonar Data Collected During EQT180001
DOI: 10.25921/0f4s-p090
title: ME70 Water Column Sonar Data Collected During HB1906
DOI: 10.25921/5c75-gf33
title: EK500 Water Column Sonar Data Collected During DS0201
DOI: 10.25921/5nzd-em48
title: EK60 Water Column Sonar Data Collected During OS1607
DOI: 10.25921/8ncq-6n28
title: EK60 Water Column Sonar Data Collected During SH1206
DOI: 10.17632/rs9c4nbb34.3
title: A machine learning approach leveraging epigenome associations identifies new Alzheimer’s disease loci

description: data repository for paper "A machine learning approach leveraging epigenome associations identifies new Alzheimer’s disease loci"
DOI: 10.25921/zsm2-ds43
title: EK60 Water Column Sonar Data Collected During NH1210
DOI: 10.25921/g1zw-s928
title: EK60 Water Column Sonar Data Collected During NYWEA-NF17-09
DOI: 10.25921/43hf-p227
title: EM710 Water Column Sonar Data Collected During W00477
DOI: 10.25921/zxnx-hf50
title: EM710 Water Column Sonar Data Collected During NF-17-08L2
DOI: 10.25921/dqt1-zv81
title: EM710 Water Column Sonar Data Collected During NF-17-08L1
DOI: 10.25921/22r0-ns55
title: EK500 Water Column Sonar Data Collected During DS0104
DOI: 10.25921/kbk7-p219
title: EK60 Water Column Sonar Data Collected During HB2007
DOI: 10.25921/1298-wy69
title: EK60 Water Column Sonar Data Collected During HB20TR
DOI: 10.25921/72mn-tz43
title: ME70 Water Column Sonar Data Collected During HB20TR
DOI: 10.25921/b3ka-h988
title: EK60 Water Column Sonar Data Collected During HB1804
DOI: 10.25921/q28k-7k26
title: EM710 Water Column Sonar Data Collected During W00475
DOI: 10.25921/8azz-k226
title: EK60 Water Column Sonar Data Collected During HB1801
DOI: 10.15468/de7pp3
title: Epizoic rotifers (Rotifera: Monogononta, Bdelloidea) from the gill chambers of Potamon fluviatile (Herbst, 1785)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Fontaneto, D., Segers, H., Melone, G. (2004): Epizoic rotifers (Rotifera: Monogononta, Bdelloidea) from the gill chambers of Potamon fluviatile (Herbst, 1785). Journal of Natural History 38 (10): 1225-1232, DOI: 10.1080/0022293031000155197, URL: http://dx.doi.org/10.1080/0022293031000155197
DOI: 10.25921/9zsf-2068
title: EK60 Water Column Sonar Data Collected During HB1806
DOI: 10.25921/adzd-2k19
title: EM122 Water Column Sonar Data Collected During MSM39
DOI: 10.25921/nv3z-dr57
title: EM710 Water Column Sonar Data Collected During H13335
DOI: 10.25921/z5a0-wr11
title: EM710 Water Column Sonar Data Collected During W00478
DOI: 10.25921/pm48-jd30
title: EM2040 Water Column Sonar Data Collected During NF1810
DOI: 10.25921/3847-1607
title: EM2040 Water Column Sonar Data Collected During NF1906
DOI: 10.25921/cn6h-3d42
title: EM710 Water Column Sonar Data Collected During NF1906
DOI: 10.25921/0smk-mz02
title: ME70 Water Column Sonar Data Collected During HB2007
DOI: 10.25921/5h98-jy07
title: EK60 Water Column Sonar Data Collected During NF1810
DOI: 10.25921/3da5-3y10
title: M3 Water Column Sonar Data Collected During NOAA-MC20-Survey
DOI: 10.25921/d9zh-9980
title: EK60 Water Column Sonar Data Collected During HB2001
DOI: 10.25921/w9cq-g049
title: EK60 Water Column Sonar Data Collected During HB1805
DOI: 10.25921/trzf-b144
title: EK60 Water Column Sonar Data Collected During HB2006
DOI: 10.25921/7p85-5923
title: EK60 Water Column Sonar Data Collected During HB1803
DOI: 10.25921/0drp-8m66
title: ME70 Water Column Sonar Data Collected During HB2001
DOI: 10.25921/dkk5-5q83
title: EK60 Water Column Sonar Data Collected During HB1802
DOI: 10.25921/kbkd-gc78
title: EM710 Water Column Sonar Data Collected During W00479
DOI: 10.25921/annx-mc80
title: ME70 Water Column Sonar Data Collected During HB2006
DOI: 10.25318/3310030801-fra
title: Obstacles à surmonter par les entreprises ou organismes au cours des trois prochains mois, selon les caractéristiques de l’entreprise

description: Obstacles à surmonter par les entreprises ou les organismes au cours des trois prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité commerciale et le propriétaire majoritaire.
DOI: 10.25318/3310030801-eng
title: Business or organization obstacles over the next three months, by business characteristics

description: Obstacles faced by the business or organization over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.5517/ccdc.csd.cc23kwxh
title: CCDC 1953617: Experimental Crystal Structure Determination

description: Related Article: Rupali Mittal, Amit Kumar, Satish Kumar Awasthi|2021|RSC Advances|11|11166|doi:10.1039/D1RA01053K
DOI: 10.25318/3310030901-eng
title: COVID-19 impact on business or organization status, by business characteristics

description: Impact of COVID-19 on the status of businesses and organizations, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.5281/zenodo.4636624
title: Gap-filling Satellite Land Surface Temperature Over Heatwave Periods with Machine Learning

description: The frequency of heatwave events has increased in recent decades because of global warming. Satellite observed Land Surface Temperature (LST) is a widely used parameter for assessing heatwaves. It provides a wide spatial coverage compared to surface air temperature measured at weather stations. However, LST quality is limited by cloud contamination. Because heatwaves have a limited temporal frame, having a full and cloud-free complement of LST for that period is necessary. In this letter, we explore gap-filling of LST using spatial features like land cover, elevation and vegetation indices in a machine learning approach. We use a seamless open and free daily vegetation index product which is paramount to the success of our study. We create a Random Forest model that provides a ranking of features relevant for predicting LST. We compare the output of our model to an established spatiotemporal gap-filling algorithm to validate the predictive capability of our model. This study validates machine learning as a suitable tool for filling gaps in satellite LST. In addition, we acknowledge that while time is an important factor in predicting LST, additional information on vegetation can improve the predictions of a model. Please refer to latest version.
DOI: 10.5281/zenodo.4593207
title: Gap-filling Satellite Land Surface Temperature Over Heatwave Periods with Machine Learning

description: The frequency of heatwave events has increased in recent decades because of global warming. Satellite observed Land Surface Temperature (LST) is a widely used parameter for assessing heatwaves. It provides a wide spatial coverage compared to surface air temperature measured at weather stations. However, LST quality is limited by cloud contamination. Because heatwaves have a limited temporal frame, having a full and cloud-free complement of LST for that period is necessary. In this letter, we explore gap-filling of LST using spatial features like land cover, elevation and vegetation indices in a machine learning approach. We use a seamless open and free daily vegetation index product which is paramount to the success of our study. We create a Random Forest model that provides a ranking of features relevant for predicting LST. We compare the output of our model to an established spatiotemporal gap-filling algorithm to validate the predictive capability of our model. This study validates machine learning as a suitable tool for filling gaps in satellite LST. In addition, we acknowledge that while time is an important factor in predicting LST, additional information on vegetation can improve the predictions of a model. Please refer to latest version.
DOI: 10.15468/e5wx3h
title: Morphological and ecological disparity in naraoiids (Arthropoda) from the Early Cambrian Chengjiang Fauna, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chen, Jun-Yuan, Edgecombe, Gregory D., Ramsköld, Lars (1997): Morphological and ecological disparity in naraoiids (Arthropoda) from the Early Cambrian Chengjiang Fauna, China. Records of the Australian Museum 49 (1): 1-24, DOI: 10.3853/j.0067-1975.49.1997.249, URL: https://journals.australian.museum/chen-et-al-1997-rec-aust-mus-491-124/
DOI: 10.25318/3310030901-fra
title: Répercussions de la COVID-19 sur l’état de l’entreprise ou de l’organisme, selon les caractéristiques de l’entreprise

description: Répercussions de la COVID-19 sur l’état de l’entreprise ou de l’organisme, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité commerciale et le propriétaire majoritaire.
DOI: 10.15468/zfhzyr
title: A tetrapod fauna from the Permian of the Sydney Basin

description: This dataset contains the digitized treatments in Plazi based on the original journal article Warren, Anne (1997): A tetrapod fauna from the Permian of the Sydney Basin. Records of the Australian Museum 49 (1): 25-33, DOI: 10.3853/j.0067-1975.49.1997.297, URL: https://journals.australian.museum/warren-1997-rec-aust-mus-491-2533/
DOI: 10.6084/m9.figshare.14349527.v1
title: Sensitivity of Western U.S. river basins to seasonally varying climate warming

description: This package includes all the representative data used in the analyses in the paper entitled "Sensitivity of Western U.S. river basins to seasonally varying climate warming", submitted by Zhaoxin Ban and Dennis P. Lettenmaier to <i>Water Resources Research.</i>
DOI: 10.15468/usure5
title: Taxonomic revision and cladistic analysis of ghost spiders of the genus Tafana Simon, 1903 (Araneae: Dionycha, Anyphaenidae), with the descriptions of twelve new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article De Oliveira, Luiz Fernando M., Brescovit, Antonio Domingos (2021): Taxonomic revision and cladistic analysis of ghost spiders of the genus Tafana Simon, 1903 (Araneae: Dionycha, Anyphaenidae), with the descriptions of twelve new species. European Journal of Taxonomy 742: 1-77, DOI: https://doi.org/10.5852/ejt.2021.742.1291, URL: http://dx.doi.org/10.5852/ejt.2021.742.1291
DOI: 10.15468/fxzy3f
title: A new species of Syscenus Harger, 1880 (Crustacea: Isopoda: Aegidae) from eastern Australia, with a revised diagnosis of the genus

description: This dataset contains the digitized treatments in Plazi based on the original journal article Bruce, Niel L. (1997): A new species of Syscenus Harger, 1880 (Crustacea: Isopoda: Aegidae) from eastern Australia, with a revised diagnosis of the genus. Records of the Australian Museum 49 (2): 113-120, DOI: 10.3853/j.0067-1975.49.1997.1261, URL: https://journals.australian.museum/bruce-1997-rec-aust-mus-492-113120/
DOI: 10.25318/3310031001-eng
title: Plans to expand or restructure business or acquire other businesses in the next year, by business characteristics

description: Plans for the business to expand or restructure, or to acquire or invest in other businesses in the next year, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310031001-fra
title: Intentions des entreprises de développer ou de restructurer leur entreprise ou d’acquérir d’autres entreprises au cours de la prochaine année, selon les caractéristiques de l’entreprise

description: Intentions des entreprises de développer ou de restructurer leur entreprise, ou d’acquérir d’autres entreprises ou d’investir dans d’autres entreprises au cours de la prochaine année, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité commerciale et le propriétaire majoritaire.
DOI: 10.3886/e119686v1-82740
title: Does Real Estate Price Affect Corporate Innovation in China?

description: The relationship between Chinese real estate prices and enterprises’ innovation is surveyed in the research. Real estate can be divided into residential real estate and commercial one in China. An empirical analysis of the data of Chinese listed companies in 2007-2018 shows that: (1) real estate prices are negatively related to corporate innovation, and (2) investment crowding-out theory and financing constraint theory cannot explain the phenomenon, which is explained by mental accounting theory, instead. In addition, with the rise-up of real estate prices, corporate innovation decreases in a short term, but rises for compensation after 1-3 years, while returning to normal level in 5 years. Finally, it is found that enterprises are more sensitive to commercial real estate prices than the price of residential one. The main contributions of this paper lie in identification of the above two major theories not suitable for Chinese enterprises and suggestion of new explanations. Future research, therefore, should avoid using investment crowding-out theory and financing constraints theory when investigating relevant Chinese topics.
DOI: 10.18112/openneuro.ds003596.v1.0.0
title: Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms
DOI: 10.25318/3310031101-eng
title: Plans to transfer, sell, or close business, by business characteristics

description: Plans for the business to transfer, sell, or close, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.6084/m9.figshare.14357009
title: ASD and CTRL path data

description: Contains id of participant (CTRL + num, ASD + num), time, behavior and patch where it happened.
DOI: 10.5281/zenodo.4654656
title: mohammadalihumayun/kadazan_digits: kadazan spoken digits v1

description: speech dataset
DOI: 10.25318/3310031101-fra
title: Intentions des entreprises de transférer, de vendre ou de fermer leur entreprise, selon les caractéristiques de l’entreprise

description: Intentions des entreprises de transférer, de vendre ou de fermer leur entreprise, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité commerciale et le propriétaire majoritaire.