cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8332107
title: ELITE land surface temperature: Global Landsat LST (2020.7.29-2020.7.31)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method. This is the ELITE Landsat LST dataset for Landsat 8 from July 29, 2020 to July 31, 2020. Please <em>click here</em> to download the ELITE Landsat LST for Landsat 8 from July 25, 2020 to July 28, 2020. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global landmsss Temporal Coverage: 2020.7.29-2020.7.31 Spatial Resolution: 30m Temporal Resolution: 16 days Data Format: Geotiff Scale: 0.01 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032 Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8332108
title: ELITE land surface temperature: Global Landsat LST (2020.7.29-2020.7.31)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method. This is the ELITE Landsat LST dataset for Landsat 8 from July 29, 2020 to July 31, 2020. Please <em>click here</em> to download the ELITE Landsat LST for Landsat 8 from July 25, 2020 to July 28, 2020. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global landmsss Temporal Coverage: 2020.7.29-2020.7.31 Spatial Resolution: 30m Temporal Resolution: 16 days Data Format: Geotiff Scale: 0.01 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032 Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.6084/m9.figshare.24210996
title: Crusher Wear Sensor Data

description: The dataset is a collection of wear sensor measurements into the gyratory crusher concave liners.
DOI: 10.4121/8ca918e1-4423-499e-be19-174562fa6973.v1
title: Data underlying the publication: Planar Delamination of CFRP Panels under Quasi-static Out-of-plane Indentation

description: The uploaded dataset collects test data related to planar delamination under quasi-static out-of-plane indentation. It includes essential components such as force-displacement data obtained from the MTS machine, as well as information on compliance/stiffness variations. A set of images used for 3D DIC analysis, facilitating the extraction of out-of-plane deflection, surface strains, and curvatures, is also provided. Moreover, ultrasound scanning images is also provided for measurement of delamination area. Additionally, an acoustic emission dataset is provided for the purpose of monitoring delamination initiation, and possibly for identifying damage modes and damage states.This comprehensive dataset has been made accessible to the research community, serving as a resource for researchers to incorporate into their own research endeavors.
DOI: 10.5281/zenodo.7806036
title: Photoelectron Spectroscopy from a Liquid Flatjet - data

description: Data set pertaining to the article "Photoelectron spectroscopy from a liquid flatjet", published in J. Chem. Phys. 158, 234202 (2023). Files with extension .h5 are hdf5-files structured according to the NeXus standard v2020.10, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)<br> Additionally, some properties of our liquid jet sample environment are described by extensions to standard NeXus explained in a notes-section in each file. In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw'). Files with extension .txt are tab-separated ascii-files. The following files are provided: Measured spectra underlying the articles' figures:<br> Figure2NEXUS.h5<br> Figure3NEXUS.h5<br> Figure4NEXUS.h5<br> Figure5NEXUS.h5<br> Figure6NEXUS.h5<br> Figure7NEXUS.h5 Numeric representation of the results shown in graphical form:<br> 'Figure 6.txt'. In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .
description: Funding acknowledgements: Deutsche Forschungsgemeinschaft (Wi 1327/5-1), Max-Water initiative of the Max-Planck-Gesellschaft, JSPS KAKENHI Grant No. JP20K15229.
DOI: 10.5281/zenodo.7806035
title: Photoelectron Spectroscopy from a Liquid Flatjet - data

description: Data set pertaining to the article "Photoelectron spectroscopy from a liquid flatjet", published in J. Chem. Phys. 158, 234202 (2023). Files with extension .h5 are hdf5-files structured according to the NeXus standard v2020.10, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)<br> Additionally, some properties of our liquid jet sample environment are described by extensions to standard NeXus explained in a notes-section in each file. In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw'). Files with extension .txt are tab-separated ascii-files. The following files are provided: Measured spectra underlying the articles' figures:<br> Figure2NEXUS.h5<br> Figure3NEXUS.h5<br> Figure4NEXUS.h5<br> Figure5NEXUS.h5<br> Figure6NEXUS.h5<br> Figure7NEXUS.h5 Numeric representation of the results shown in graphical form:<br> 'Figure 6.txt'. In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .
description: Funding acknowledgements: Deutsche Forschungsgemeinschaft (Wi 1327/5-1), Max-Water initiative of the Max-Planck-Gesellschaft, JSPS KAKENHI Grant No. JP20K15229.
DOI: 10.6084/m9.figshare.23590302
title: Reconstructing visual illusory experiences from human brain activity

description: This repository provides preprocessed fMRI data, stimulus image features, and pretrained image generators used in the following work.Cheng, F., Horikawa, T., Majima, K., Tanaka, M., Abdelhack, M., Aoki, S. C., Hirano, J., & Kamitani, Y. (2023). Reconstructing visual illusory experiences from human brain activity. bioRxiv. https://doi.org/10.1101/2023.06.15.545037Raw (unpreprocessed) fMRI data are available at https://openneuro.org/datasets/ds004670.Code is available at https://github.com/KamitaniLab/IllusionReconstruction.See the GitHub repository for more details.History2023-09-24 Initial release2023-09-28 Update generators (remove unnecessary files in generator-GAN.zip and add generator-diffusion.zip)
DOI: 10.58119/ulg/qjj0sa/6czbwt
title: APN_description_english.pdf

description: :unav
DOI: 10.58119/ulg/qjj0sa/6s09kq
title: APN_description_french.pdf

description: :unav
DOI: 10.58119/ulg/qjj0sa/w2vnpn
title: SubordinationTags_APN.doc

description: :unav
DOI: 10.58119/ulg/qjj0sa/3pdbvc
title: MorphosyntacticTagsAPN.Doc

description: :unav
DOI: 10.58119/ulg/qjj0sa/k9cknd
title: LASLA_Dictionary.pdf

description: :unav
DOI: 10.5281/zenodo.8132385
title: Dataset for "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases"

description: This repository contains data and numerical simulation code for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases". The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environment such as python (h5py), matlab (`h5read`), and many others. Numerical simulation scripts consist of functions.c, MonteCarloSampling.cpp and phase12_den_sample_time.cpp. Please see the section for numerical simulation in the Supplementary Material. The description for each hdf5 file in this repository is provided in readme.txt.
DOI: 10.5281/zenodo.8132386
title: Dataset for "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases"

description: This repository contains data for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases". The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environments such as Python (h5py), Matlab (`h5read`), and many others. The description of hdf5 files in this repository is provided in readme.txt.
DOI: 10.5281/zenodo.8237752
title: Dataset for "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases"

description: This repository contains data and numerical simulation code for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases". The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environment such as python (h5py), matlab (`h5read`), and many others. Numerical simulation scripts consist of functions.c, MonteCarloSampling.cpp and phase12_den_sample_time.cpp. Please see the section for numerical simulation in the Supplementary Material. The description for each hdf5 files in this repository is provided in readme.txt.
DOI: 10.5281/zenodo.8385524
title: Dataset for "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases"

description: This repository contains data and numerical simulation code for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases". The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environment such as python (h5py), matlab (`h5read`), and many others. Numerical simulation scripts consist of functions.c, MonteCarloSampling.cpp and phase12_den_sample_time.cpp. Please see the section for numerical simulation in the Supplementary Material. The description for each hdf5 file in this repository is provided in readme.txt.
DOI: 10.6084/m9.figshare.24210999.v1
title: Optical screen perturbation data

description: Optical screen perturbation data
DOI: 10.6084/m9.figshare.24210999
title: Optical screen perturbation data

description: Optical screen perturbation data
DOI: 10.5281/zenodo.8380814
title: Suspended sediment load and bedload flux from the Glacier d'Otemma proglacial forefield (summers 2020 and 2021)

description: The Glacier d’Otemma proglacial margin, located in the Swiss Alps at an altitude of about 2450 m a.s.l. (45.93423 N, 7.41160 E), is characterized by a ca. 1 km long by 200 m wide active braided forefield. In this setting we installed two gauging stations for the monitoring of both suspended sediment and bedload transport within the proglacial margin: GS1 at about 350 m from the glacier terminus and GS2 at the forefield outlet. Monitoring stations were equipped with water pressure sensors (CS451 from Cambell Scientific), turbidity probes (OBS300+ from Cambell Scientific) and geophones (3-components PE-6/B from Sensor Nederland connected to a DiGOS DATA-CUBE type 2 logger). Water discharge were determined following modalities described in Müller and Miesen (2022). Suspended loads were quantified using a conventional turbidity-suspended sediment concentration relationship, while bedload transport was derived seismically using the geophysical Fluvial model inversion (FMI) algorithm developed in Dietze et al. (2018). The dataset covers summers 2020 and 2021. Further details on data aquisition and post-processing techniques are available in Mancini et al. (2023).
DOI: 10.34740/kaggle/dsv/6561090
title: OnlineFree
DOI: 10.34740/kaggle/dsv/6561089
title: AutoXGB
DOI: 10.34740/kaggle/dsv/4991874
title: OTTO Recommender Systems Dataset
DOI: 10.5066/f7bk1b77
title: Cottonwood Lake Study Area - Invertebrate Counts

description: This dataset contains macroinvertebrate sampling data for the wetlands in the Cottonwood Lake Study Area, Stutsman County, North Dakota. This dataset is part of the Missouri Coteau Wetland Ecosystem Observatory database that is a collection of abiotic and biotic data documenting biotic and abiotic responses to long-term climate and land-use change in the Prairie Pothole Region (PPR) of the U.S. The PPR has a naturally dynamic climate that is characterized by extended wet and dry periods, and its wetlands have spatially variable hydrologic and biotic traits. The Cottonwood Lake Study Area in Stutsman County, North Dakota contains wetlands representative of multiple positions along a hydrologic continuum, from groundwater recharge to discharge. The extensive history of data collection in the Cottonwood Lakes Study Area provides a unique opportunity to assess the impact of climate variability on ecosystem health and productivity.
DOI: 10.5441/001/1.282vr7kd
title: Data from: Study "Herring Gulls (Larus Argentatus); Ronconi; Brier Island, Canada"

description: Anderson CM, Gilchrist HG, Ronconi RA, Shlepr KR, Clark DE, Fifield DA, Robertson GJ, Mallory ML (2020) Both short and long distance migrants use energy-minimizing strategies in North American herring gulls. Movement Ecology. doi:10.1186/s40462-020-00207-9
description: Background: Recent studies have proposed that birds migrating short distances migrate at an overall slower pace, minimizing energy expenditure, while birds migrating long distances minimize time spent on migration to cope with seasonal changes in environmental conditions. Methods: We evaluated variability in the migration strategies of Herring Gulls (Larus argentatus), a generalist species with flexible foraging and flight behaviour. We tracked one population of long distance migrants and three populations of short distance migrants, and compared the directness of their migration routes, their overall migration speed, their travel speed, and their use of stopovers. Results: Our research revealed that Herring Gulls breeding in the eastern Arctic migrate long distances to spend the winter in the Gulf of Mexico, traveling more than four times farther than gulls from Atlantic Canada during autumn migration. While all populations used indirect routes, the long distance migrants were the least direct. We found that regardless of the distance the population traveled, Herring Gulls migrated at a slower overall migration speed than predicted by Optimal Migration Theory, but the long distance migrants had higher speeds on travel days. While long distance migrants used more stopover days overall, relative to the distance travelled all four populations used a similar number of stopover days. Conclusions: When taken in context with other studies, we expect that the migration strategies of flexible generalist species like Herring Gulls may be more influenced by habitat and food resources than migration distance.
DOI: 10.6084/m9.figshare.24199788
title: Anexos.A.Familia.pdf

description: This dataset was colllected as part of a PhD project funded by Consejo Nacional de Ciencia y Tecnología. Qualitative methods were used to interview 18 adolescent migrants travelling alone through Mexico - mostly undocumented - and 29 workers in services for or with unaccompanied migrant youth in two migrant shelters in Mexico in June and July 2021. Interviews were transcribed and analysed with the NVivo software using reflexive thematic analysis. The data presented here shows an anonymised summary of participant demographics and the tools used to interview participantsData included in the file is:1. Anonymised demographic information of migrant adolescent participants2. Anonymised roles of professional workers interviewed3. Initial questionnaire asked to migrant adolescents during the interview (answers summarised in their demographic characteristics)4. Topic guide for semi-structured interviews with migrant youth and workers5. Reflexivity of main author and project lead (Susanna Corona Maioli) and research assistants (Scarlett Machado and Jessica Gómez)<br>
DOI: 10.6084/m9.figshare.24199788.v2
title: Anexos.A.Familia.pdf

description: This dataset was colllected as part of a PhD project funded by Consejo Nacional de Ciencia y Tecnología. Qualitative methods were used to interview 18 adolescent migrants travelling alone through Mexico - mostly undocumented - and 29 workers in services for or with unaccompanied migrant youth in two migrant shelters in Mexico in June and July 2021. Interviews were transcribed and analysed with the NVivo software using reflexive thematic analysis. The data presented here shows an anonymised summary of participant demographics and the tools used to interview participantsData included in the file is:1. Anonymised demographic information of migrant adolescent participants2. Anonymised roles of professional workers interviewed3. Initial questionnaire asked to migrant adolescents during the interview (answers summarised in their demographic characteristics)4. Topic guide for semi-structured interviews with migrant youth and workers5. Reflexivity of main author and project lead (Susanna Corona Maioli) and research assistants (Scarlett Machado and Jessica Gómez)<br>
DOI: 10.13140/rg.2.2.35819.31529
title: WETLAND MAPPING
DOI: 10.34740/kaggle/dsv/6561172
title: Egypt Gold Prices | Daily Updated
DOI: 10.57745/apolxy
title: Taux_dependance_nette.xls

description: Fichier contenant les résultats en termes de taux de dépendance nette (importations nettes / utilisations domestiques totales) calculés en équivalent calories
DOI: 10.57745/gsxvna
title: SYNTHESE-BILANS_ANMO.xls

description: Fichier contenant les données pour chaque scénario et pour toutes les variables de GlobAgri
DOI: 10.13140/rg.2.2.14847.79529
title: الخريطة الجيولوجية للجمهورية العربية السورية إعداد: د.يارا الويش The Geological map of Syria
DOI: 10.17632/hhkt76b36b.1
title: Geochemical data for CL6

description: Recently, the redox sensitive behaviour and associated isotope fractionations of molybdenum in various marine geological environments has received much attention, but the characteristics of δ98Mo in cold-seep system are still not fully understood. Therefore, we conducted a series of geochemical analyses including Mo isotopes on the sediment samples (CL6) obtained from the “Haima” cold-seep deposit area in the South China Sea. The results show that the 373–403cm in CL6 is obviously affected by methane seepage with a light δ98Mo signature. Comprehensive analysis shows that sulfidic dissolution of Fe and Mn (oxy)hydroxides is the fundamental reason for this phenomenon. This not only deepens our understanding of Mo geochemical cycles, but also reminds us to be more rigorous when using δ98Mo for paleo-redox environment reconstruction.
DOI: 10.17632/r2rfktpj4c.1
title: Supplemental Material for: Artificial Intelligence Skin Cancer Publications from Dermatology Journals

description: Supplemental Material for: Artificial Intelligence Skin Cancer Publications from Dermatology Journals Data retrieved from the Web of Science on June 1, 2022. All artificial intelligence skin cancer publications from dermatology journals were included.