cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25346/s6eecgix/rgdl79
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DOI: 10.25346/s6eecgix/hxfcdl
title: msXL_334r_31_24.jpg
DOI: 10.26023/fst7-9hyc-m314
title: ASOS 1-Minute Data. Version 1.0

description: 1--minute resolution surface meteorological observations from the Automated Surface Observing System (ASOS) network of ~890 stations in the United States and select locations elsewhere. These data were collected by the National Centers for Environmental Information.
DOI: 10.25346/s6eecgix/drzse6
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DOI: 10.25346/s6eecgix/20ozen
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DOI: 10.25346/s6eecgix/s0vwqi
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DOI: 10.25346/s6eecgix/do5yne
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DOI: 10.25346/s6eecgix/u3weyg
title: msXL_323r_0_ring.tif
DOI: 10.25346/s6eecgix/5xpxxk
title: msXL_323r.tif
DOI: 10.25346/s6eecgix/vgbvlp
title: skin.svg
DOI: 10.25346/s6eecgix/unhzoc
title: skin.css
DOI: 10.25346/s6eecgix/7ybgz4
title: plane_3.jpg
DOI: 10.25346/s6eecgix/cytift
title: plane_2.jpg
DOI: 10.25346/s6eecgix/kb3afw
title: plane_1.jpg
DOI: 10.25346/s6eecgix/bejjwf
title: plane_0.jpg
DOI: 10.25346/s6eecgix/fo4dy0
title: openlime.min.js
DOI: 10.25346/s6eecgix/gszqij
title: materials.png
DOI: 10.25346/s6eecgix/lel8c0
title: info.json
DOI: 10.25346/s6eecgix/ijvhrw
title: index.html
DOI: 10.25346/s6eecgix/8jwiy7
title: skin.svg
DOI: 10.25346/s6eecgix/wkuhgq
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DOI: 10.25346/s6eecgix/vr0z6c
title: openlime.min.js
DOI: 10.25346/s6eecgix/x653xl
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DOI: 10.25346/s6eecgix/hnv5bn
title: info.json
DOI: 10.25346/s6eecgix/8oce9g
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DOI: 10.25346/s6eecgix/7khfx8
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DOI: 10.25346/s6eecgix/njtgru
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DOI: 10.25346/s6eecgix/a8pk4j
title: openlime.min.js
DOI: 10.25346/s6eecgix/7cgbou
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DOI: 10.25346/s6eecgix/lnmyem
title: info.json
DOI: 10.25346/s6eecgix/e3srrm
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DOI: 10.25346/s6eecgix/rbp6af
title: openlime.min.js
DOI: 10.25346/s6eecgix/1ymhaj
title: info.json
DOI: 10.25346/s6eecgix/s5gaa6
title: index.html
DOI: 10.25346/s6eecgix/kfwlyh
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DOI: 10.25346/s6eecgix/un2vbm
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DOI: 10.25346/s6eecgix/crzgjv
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DOI: 10.25346/s6eecgix/6rbfaf
title: openlime.min.js
DOI: 10.25346/s6eecgix/wgvu5l
title: info.json
DOI: 10.25346/s6eecgix/lje4g2
title: index.html
DOI: 10.25346/s6eecgix/qmtaru
title: skin.svg
DOI: 10.25346/s6eecgix/ccn8yd
title: skin.css
DOI: 10.25346/s6eecgix/v9jckw
title: plane_8.jpg
DOI: 10.25346/s6eecgix/hoipze
title: plane_7.jpg
DOI: 10.25346/s6eecgix/pc9ny7
title: plane_6.jpg
DOI: 10.26023/9d5f-nz8h-ys0a
title: ASOS 5-Minute Data. Version 1.0

description: 5-minute resolution surface meteorological observations from the Automated Surface Observing System (ASOS) network of ~890 stations in the United States and select locations elsewhere. These data were collected by the National Centers for Environmental Information.
DOI: 10.25346/s6eecgix/qxzqav
title: plane_5.jpg
DOI: 10.25346/s6eecgix/uxezju
title: plane_4.jpg
DOI: 10.25346/s6eecgix/51sjls
title: plane_3.jpg
DOI: 10.25346/s6eecgix/7eteob
title: plane_2.jpg
DOI: 10.25346/s6eecgix/qa6jod
title: plane_1.jpg
DOI: 10.25346/s6eecgix/vq6kku
title: plane_0.jpg
DOI: 10.25346/s6eecgix/k5hdeh
title: openlime.min.js
DOI: 10.25346/s6eecgix/kqcgkw
title: materials.png
DOI: 10.25346/s6eecgix/amfbld
title: info.json
DOI: 10.25346/s6eecgix/1mt6ko
title: index.html
DOI: 10.25346/s6eecgix/2msgna
title: msXL_323r.relight
DOI: 10.25346/s6eecgix/wbsn2v
title: msXL_323r_65_49.jpg
DOI: 10.25346/s6eecgix/zu1ivk
title: msXL_323r_65_48.jpg
DOI: 10.25346/s6eecgix/3lyvyt
title: msXL_323r_65_47.jpg
DOI: 10.25346/s6eecgix/hapari
title: msXL_323r_65_46.jpg
DOI: 10.25346/s6eecgix/vrgtvr
title: msXL_323r_65_45.jpg
DOI: 10.25346/s6eecgix/9ld2mw
title: msXL_323r_65_44.jpg
DOI: 10.25346/s6eecgix/8kemmm
title: msXL_323r_65_43.jpg
DOI: 10.25346/s6eecgix/ext98n
title: msXL_323r_65_42.jpg
DOI: 10.25346/s6eecgix/8m6vo5
title: msXL_323r_65_41.jpg
DOI: 10.25346/s6eecgix/v6hmyp
title: msXL_323r_65_40.jpg
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DOI: 10.25346/s6eecgix/4hcdbj
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DOI: 10.25346/s6eecgix/wd6ujc
title: msXL_323r_65_37.jpg
DOI: 10.25346/s6eecgix/uuxp1x
title: msXL_323r_48_36.jpg
DOI: 10.25346/s6eecgix/ir2jzy
title: msXL_323r_48_35.jpg
DOI: 10.25346/s6eecgix/8ctcxc
title: msXL_323r_48_34.jpg
DOI: 10.25346/s6eecgix/pzokkp
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DOI: 10.25346/s6eecgix/deajf7
title: msXL_323r_48_32.jpg
DOI: 10.25346/s6eecgix/sh8m29
title: msXL_323r_48_31.jpg
DOI: 10.25346/s6eecgix/isffyx
title: msXL_323r_48_30.jpg
DOI: 10.25346/s6eecgix/eb1ctl
title: msXL_323r_48_29.jpg
DOI: 10.25346/s6eecgix/jwvrhh
title: msXL_323r_48_28.jpg
DOI: 10.25346/s6eecgix/eda8mo
title: msXL_323r_48_27.jpg
DOI: 10.25346/s6eecgix/gb5qus
title: msXL_323r_48_26.jpg
DOI: 10.25346/s6eecgix/mdd0bh
title: msXL_323r_48_25.jpg
DOI: 10.25346/s6eecgix/hrdbnm
title: msXL_323r_31_24.jpg
DOI: 10.25346/s6eecgix/3e0boa
title: msXL_323r_31_23.jpg
DOI: 10.25346/s6eecgix/dco2au
title: msXL_323r_31_22.jpg
DOI: 10.25346/s6eecgix/q8o2fw
title: msXL_323r_31_21.jpg
DOI: 10.25346/s6eecgix/uzvebj
title: msXL_323r_31_20.jpg
DOI: 10.25346/s6eecgix/gpqtyu
title: msXL_323r_31_19.jpg
DOI: 10.25346/s6eecgix/50mytj
title: msXL_323r_31_18.jpg
DOI: 10.25346/s6eecgix/nuzjbl
title: msXL_322v_31_16.jpg
DOI: 10.25346/s6eecgix/lad5ff
title: msXL_322v_31_15.jpg
DOI: 10.25346/s6eecgix/xz2sot
title: msXL_322v_31_14.jpg
DOI: 10.25346/s6eecgix/izrnyv
title: msXL_322v_31_13.jpg
DOI: 10.25346/s6eecgix/akypnu
title: msXL_322v_15_12.jpg
DOI: 10.25346/s6eecgix/lc01pe
title: msXL_322v_15_11.jpg
DOI: 10.25346/s6eecgix/jxkfcw
title: msXL_322v_15_10.jpg
DOI: 10.25346/s6eecgix/kf1u42
title: msXL_322v_15_09.jpg
DOI: 10.25346/s6eecgix/5kqdjm
title: msXL_322v_15_08.jpg
DOI: 10.26023/yghp-za38-hv0t
title: TI3GER Chatlogs. Version 1.0

description: Flight Chat Logs for the TI3GER (Technological Innovation Into Iodine and GV Environmental Research) field project, collected from 01 April to 29 April 2022 onboard the NSF/NCAR GV (Gulfstream V) HIAPER. These data are available in a txt format.
DOI: 10.26023/ecnv-d1vd-8409
title: NOAA/PSL S-Band Precipitation Profiling Radar Data. Version 2.0

description: Data from the NOAA/PSL S-band precipitation profiling radar that was deployed at Courtland, Alabama for the PERiLS 2022 and continuing to 9 February 2023 when measurements concluded. Measurements include vertical profiles of radial velocity, spectrum width, SNR, and power.
DOI: 10.26023/bmq8-ksmh-s208
title: WINTRE-MIX: NRC Convair 580 Airborne W-Band (NAW) Radar Data and Imagery. Version 1.0

description: NRC Convair 580 Airborne W-Band (NAW) radar data and imagery are included in this dataset. This dataset was collected during the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project between February and March 2022 using an airborne W-band (NAW) 94.05 GHz radar. The NAW radar antenna subsystem was housed inside an un-pressurized blister radome on the aircraft fuselage. In normal operational mode, these radar data are available at nadir, zenith and side antennas. When flying at low altitude, the nadir antennas are terminated to avoid damaging RF components. For WINTRE-MIX, the radar has a dedicated multiple Inertial Measurement Unit (IMU) system installed at the radar rack. Both data and imagery are provided together in zip files per flight.
DOI: 10.26023/atf0-abh6-9j0g
title: WINTRE-MIX: NRC Convair 580 Airborne X-Band (NAX) Radar Data and Imagery. Version 1.0

description: NRC Convair 580 Airborne X-Band (NAX) Radar Data and Imagery are included in this dataset. This dataset was collected during the Wintre Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project between February and March 2022 using an airborne X-band (NAX) 9.41 GHz radar. The NAX radar antenna subsystem was housed inside an un-pressurized blister radome on the aircraft fuselage. In normal opertional mode, these radar data are available at nadir, zenith and side antennas. When flying at low altitude, the nadir antennas are terminated to avoid damaging RF components. For WINTRE-MIX, the radar has a dedicated multiple Inertial Measurement Unit (IMU) system installed at the radar rack. Both data and imagery are provided in a single zip file per flight.
DOI: 10.26023/xdkv-qxc2-1y0j
title: SWEX: ISFS Surface Meteorology and Flux Products - georeferenced. Version 1.0

description: Surface meteorology and flux products collected by the EOL/Integrated Surface Flux System team during the Sundowner Wind Experiment (SWEX) in Santa Barbara, CA. Data have been collected from 18 towered sites spread out over the leeward side of the Santa Ynez Mountains in Santa Barbara County, CA. The data set is a merged product of all sties into * 5 minute averaged resolution, and * high rate data that varies from 50 ms for 20 Hz sensors to 1s for 1 Hz sensors. Refer to the data report for more information.
DOI: 10.5281/zenodo.17282448
title: MalayaNetwork_GT: Non-IID dataset for FL-Based NTC Evaluation

description: And inquiries: mazizi@fskm.uitm.edu.my
DOI: 10.5281/zenodo.17282449
title: MalayaNetwork_GT: Non-IID dataset for FL-Based NTC Evaluation

description: And inquiries: mazizi@fskm.uitm.edu.my
DOI: 10.5281/zenodo.17282452
title: Quantum gravity and dark matter
DOI: 10.5281/zenodo.17282453
title: Quantum gravity and dark matter
DOI: 10.26023/jvjd-ej4y-fq0z
title: PERiLS_2022: SMART-R1 Radar Data. Version 1.0

description: Observations from the University of Oklahoma SMART-R1 (Shared Mobile Atmospheric Research and Teaching Radar 1) mobile polarimetric Doppler radar that was deployed at locations around the southeastern United States for the PERiLS_2022 deployments that occurred on 22 March and 13 April 2022.
DOI: 10.6084/m9.figshare.30291062
title: Underwater image classification and object detection using shuffle convolutional channel enhancement

description: The complexity of the underwater environment poses significant challenges to underwater visual fields such as underwater object classification and detection, due to factors such as light attenuation, colour distortion, blurriness, noise, and complex underwater backgrounds. This paper proposes a local attention algorithm using Shuffle Convolutional Channel enhancement (SF-NN), which improves the accuracy of key feature identification in images.The SF-NN technique effectively addresses variations in the scale of visual entities and high-resolution issues, improving connectivity between windows and significantly enhancing the accuracy of underwater image classification,and it was verified on the ImageNet and FishNet datasets. Furthermore, by integrating the proposed algorithm as a feature extraction method into the Mask R-CNN framework, we effectively tackle problems such as object occlusion and deformation in complex underwater scenes, thereby improving the precision and robustness of the Mask R-CNN object detection algorithm.It has been validated on the COCO and TransCan datasets, demonstrating the significant advantages of the algorithm in improving underwater object detection performance through applications in different underwater environments and targets.
DOI: 10.6084/m9.figshare.30291062.v1
title: Underwater image classification and object detection using shuffle convolutional channel enhancement

description: The complexity of the underwater environment poses significant challenges to underwater visual fields such as underwater object classification and detection, due to factors such as light attenuation, colour distortion, blurriness, noise, and complex underwater backgrounds. This paper proposes a local attention algorithm using Shuffle Convolutional Channel enhancement (SF-NN), which improves the accuracy of key feature identification in images.The SF-NN technique effectively addresses variations in the scale of visual entities and high-resolution issues, improving connectivity between windows and significantly enhancing the accuracy of underwater image classification,and it was verified on the ImageNet and FishNet datasets. Furthermore, by integrating the proposed algorithm as a feature extraction method into the Mask R-CNN framework, we effectively tackle problems such as object occlusion and deformation in complex underwater scenes, thereby improving the precision and robustness of the Mask R-CNN object detection algorithm.It has been validated on the COCO and TransCan datasets, demonstrating the significant advantages of the algorithm in improving underwater object detection performance through applications in different underwater environments and targets.
DOI: 10.26023/12rc-abeq-8j0h
title: PERiLS_2022: SMART-R2 Radar Data. Version 1.0

description: Observations from the University of Oklahoma SMART-R2 (Shared Mobile Atmospheric Research and Teaching Radar 2) mobile polarimetric Doppler radar that was deployed at locations around the southeastern United States for the PERiLS_2022 deployments that occurred on 22 March and 13 April 2022.
DOI: 10.26023/901n-tjr6-5d02
title: TRANS2AM: Tahoe Mobile Plume Tracking Vehicle 2022 Phase Measurements. Version 1.0

description: Data collected from several instruments that were on-board the Colorado State University Tahoe mobile plume tracking vehicle during Phase 2 (August and September 2022) of the TRANS2AM campaign. Included are data from a Picarro G2508 Analyzer (methane, ammonia, carbon dioxide, and water vapor), a Particle Measuring Systems Air Sentry Ammonia Analyzer (ammonia), and a meteorological mast (surface winds, air temperature, and relative humidity.
DOI: 10.34740/kaggle/dsv/13284723
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.26023/jjpf-h148-cj0h
title: TRANS2AM: King Air QC-TILDAS Gas Phase NH3 2022 Phase Data. Version 1.0

description: QC-TILDAS gas-phase ammonia (NH3) measurements collected aboard the University of Wyoming King Air aircraft during Phase 2 (August and September 2022) of the TRANS2AM campaign. The base of operations was the Laramie Airport (KLAR) in Laramie, Wyoming with most flights over northeastern Colorado.
DOI: 10.6084/m9.figshare.29401220.v1
title: Compositional selection of phospholipid compartments in icy environments drives the inheritance of encapsulated genetic information [Dataset]

description: Source data for figures in “Compositional selection of phospholipid compartments in icy environments drives the inheritance of encapsulated genetic information”.
DOI: 10.26023/fwh1-08fr-6s09
title: LEE: GOES-16 Geostationary Lightning Mapper (GLM) Level 2 Satellite Data. Version 1.0

description: NOAA GOES-16 Geostationary Lightning Mapper (GLM) Level 2 satellite data are included in this Lake Effect Electrification (LEE) Field Project dataset. These data were collected during the LEE project field phase. The Lightning Detection Gridded product generates fields starting from the GLM Lightning Detection Events, Groups, Groups, Flashes product. It consists of flash extent density, event density, average flash area, average group area, total energy, flash centroid density, and group centroid density. Please see the sample netCDF header file in the documentation section of this web page for a list of parameters included in this dataset. The data are stored in gzipped tar files by day. For the complete reference, use the following: Bruning, E., Tillier, C. E., Edgington, S. F., Rudlosky, S. D., Zajic, J., Gravelle, C., et al. (2019). Meteorological imagery for the geostationary lightning mapper. Journal of Geophysical Research: Atmospheres, 124, 14,258–14,309. https://doi.org/10.1029/2019JD030874 This is a large dataset. Please only order the days needed.
DOI: 10.5281/zenodo.17282508
title: wrist - BIDS-EMG Dataset

description: Wrist Control: sEMG dataset for wrist movement decoding
DOI: 10.48331/scielodata.vkbsus/uc53fq
title: taxas_sc_completa.tab

description: Contém os dados de caracterização sociodemográfica e as taxas de incidência de sífilis congênita utilizadas para gerar a Tabela 1, Figura 1 e Figura 2
DOI: 10.48331/scielodata.vkbsus/cjxul4
title: readme.txt
DOI: 10.48331/scielodata.vkbsus/k5sz06
title: teste_t_sixpack_normal_.rar

description: Contém os dados encontrados na analise estatística do teste t de Student
DOI: 10.48331/scielodata.vkbsus/mzz9tc
title: figura_2_cinza-1.tab

description: Contém os dados utilizados para gerar o gráfico da taxa de incidência média padronizada de sífilis congênita por Unidades Federativas e Capitais (Figura 2)
DOI: 10.48331/scielodata.vkbsus/d1nxb5
title: predicao_grafico1.tab

description: Contém os dados utilizados para gerar as projeções das taxas de incidência de sífilis congênita no Brasil e macrorregiões brasileiras com cores (Figura 3)
DOI: 10.48331/scielodata.vkbsus/ca8keq
title: figura_3_cinza-1.tab

description: Contém os dados utilizados para gerar as projeções das taxas de incidência de sífilis congênita no Brasil e macrorregiões brasileiras (Figura 3)
DOI: 10.48331/scielodata.vkbsus/ecxiwt
title: teste_t.png

description: Contém os dados encontrados na analise estatística do teste t de Student
DOI: 10.48331/scielodata.vkbsus/sclqvb
title: figura_1_cinza-1.tab

description: Contém os dados utilizados para gerar o gráfico da evolução temporal das taxas de incidência de sífilis congênita no Brasil e macrorregiões brasileiras (Figura 1)
DOI: 10.48331/scielodata.vkbsus
title: Tendência e predição da sífilis congênita no Brasil no século XXI: Um estudo de séries temporais

description: Trata-se do banco de dados do artigo intitulado "Tendência e predição da sífilis congênita no Brasil no século XXI: Um estudo de séries temporais"que foi submetido e aceito na Revista Gaúcha de Enfermagem. Trata-se de um estudo ecológico, de séries temporais, que utilizou dados secundários extraídos do Departamento de Informática do Sistema Único de Saúde no período de 2001 a 2022. Foi realizada a análise da evolução temporal da sífilis congênita utilizando o cálculo de incidência bruta e a taxa de incidência média padronizada. Para as análises de série temporal e projeções dos casos utilizou-se técnicas estatísticas como Joinpoint e Média Móvel Integrada Autorregressiva.