cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/95wcn8/vdmavo
title: data_upload.tab

description: This dataset contains population densities and observed group sizes of primate species in Lake Manyara National Park, Tanzania. The first and second datasheets contain point estimates ("Density") and associated 95% confidence intervals ("lower"; "upper") of Manyara monkeys and Hilgert's vervet monkey. Densities were estimated for each season (LR=Long rains; Dry = Dry season; SR= Short rains) in the years 2011 to 2019. The group size datasheet contains data on the observed group sizes of each target species during the seasonal surveys.
DOI: 10.25625/95wcn8
title: Replication Data for: Population dynamics of Manyara monkey Cercopithecus mitis manyaraensis and vervet monkey Chlorocebus pygerythrus in Lake Manyara National Park, Tanzania

description: This dataset contains population densities and observed group sizes of primate species in Lake Manyara National Park, Tanzania. The first and second datasheets contain point estimates ("Density") and associated 95% confidence intervals ("lower"; "upper") of Manyara monkeys and vervet monkey. Densities were estimated for each season (LR=Long rains; Dry = Dry season; SR= Short rains) in the years 2011 to 2019. The group size datasheet contains data on the observed group sizes of each target species during the seasonal surveys.
DOI: 10.5066/p97fry3m
title: Indium deposits in the United States

description: This U.S. Geological Survey (USGS) data release provides the descriptions of the only U.S. sites-including mining districts, mineral occurrences, and mine features-that have reported production and (or) resources of indium (In). This dataset contains the Bingham and West Desert deposits in Utah, and the Chino site in New Mexico. Indium is considered a critical and strategic mineral because of its use in the aerospace, defense, energy, and telecommunications sectors. The primary applications are flat-panel displays, and specialty alloys (Fortier and others, 2018). In 2021, the U.S. was 100 percent net import reliant on indium from China, Canada, Republic of Korea, and France (U.S. Geological Survey, 2022). Indium is most commonly recovered from sphalerite, a zinc-sulfide mineral, wherein the indium occurs in quantities of less than 1 part per million (ppm) to 100 ppm (U.S. Geological Survey, 2022). In the U.S., indium is found in porphyry and skarn deposits. The West Desert deposit in Utah is the only deposit in the U.S. with a modern National Instrument 43-101 (NI 43-101) compliant resource estimate of indium (Dyer and others, 2014). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as NI 43-101. The presence of an indium mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the largest indium deposits in the United States. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Dyer, T.L., Tietz, P.G., and Austin, J.B., 2014, Technical report on the West Desert zinc-copper-indium-magnetite project, preliminary economic assessment, Juab County, Utah, prepared for InZinc Mining Ltd. [Filing date March 17, 2014]: Mine Development Associates, 188 p., accessed March 10, 2020, at http://www.sedar.com. Fortier, S.M., Nassar, N.T., Lederer, G.W., Brainard, J., Gambogi, J., and McCullough, E.A., 2018, Draft critical mineral list-Summary of methodology and background information-U.S. Geological Survey technical input document in response to Secretarial Order No. 3359: U.S. Geological Survey Open-File Report 2018-1021, 15 p., https://doi.org/10.3133/ofr20181021. U.S. Geological Survey, 2022, Mineral commodity summaries 2022: U.S. Geological Survey, 202 p., https://doi.org/10.3133/mcs2022.
DOI: 10.6084/m9.figshare.20533011
title: SupplementaryTable_1

description: Expression data details
DOI: 10.6084/m9.figshare.21081643
title: Drug Perturbation Profiles

description: Cell lines were perturbed with compounds at sublethal concentrations, with a focus on antineoplastic drugs. After 24 hours of treatment (as well as 6 hours in some cell lines), cells were harvested and RNASeq performed on a multiplexed platform (PLATEseq). Post-perturbation RNASeq expression signatures were analyzed by cancer-type specific networks using VIPER, to generate post-perturbation protein activity signatures. These protein activity signatures are available here, representing de novo, experimentally-assessed, context specific drug mechanism of action. <br> These signatures were subsequently used to identify drugs predicted to invert Master Regulator signatures of individual tumors (OncoTreat algorithm).
DOI: 10.6084/m9.figshare.20247279
title: Dataset S5 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of the lncRNAs and coding genes that conform WGCNA modules
DOI: 10.6084/m9.figshare.20247279.v1
title: Dataset S5 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of the lncRNAs and coding genes that conform WGCNA modules
DOI: 10.6084/m9.figshare.19447583.v1
title: LC-UV Chromatogram

description: LC-UV chromatogram of mamba (<i>D. polylepis and D. angusticeps</i>) and cobra (<i>N. naja, N. pallida, N. nigricollis, N. haje, and H. haemachatus</i>) venom. the data was obtained from RPLC-UV Shimadzu system with gradient elution.
DOI: 10.6084/m9.figshare.19397903
title: Mascot Generic Files of cobra venoms

description: Mascot Generic Files (MGF) of Naja naja, Naja pallida, Naja nigrollis, Naja haje, and Hemachatus haemachatus
DOI: 10.6084/m9.figshare.20247273
title: Dataset S4 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of <em>Tau</em> values for the tissue-specific lncRNAs
DOI: 10.6084/m9.figshare.20247273.v1
title: Dataset S4 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of <em>Tau</em> values for the tissue-specific lncRNAs
DOI: 10.6084/m9.figshare.19447583
title: LC-UV Chromatograms

description: LC-UV chromatogram of mamba (<i>D. polylepis and D. angusticeps</i>) and cobra (<i>N. naja, N. pallida, N. nigricollis, N. haje, and H. haemachatus</i>) venom. the data was obtained from RPLC-UV Shimadzu system with gradient elution.
DOI: 10.6084/m9.figshare.19447583.v2
title: LC-UV Chromatograms

description: LC-UV chromatogram of mamba (<i>D. polylepis and D. angusticeps</i>) and cobra (<i>N. naja, N. pallida, N. nigricollis, N. haje, and H. haemachatus</i>) venom. the data was obtained from RPLC-UV Shimadzu system with gradient elution.
DOI: 10.6084/m9.figshare.19447583.v3
title: LC-UV Chromatograms

description: LC-UV chromatogram of mamba (<i>D. polylepis and D. angusticeps</i>) and cobra (<i>N. naja, N. pallida, N. nigricollis, N. haje, and H. haemachatus</i>) venom. the data was obtained from RPLC-UV Shimadzu system with gradient elution.
DOI: 10.6084/m9.figshare.19397852
title: Mascot Generic Files of mamba venoms

description: Mascot Generic Files of Dendroaspis angusticeps and Dendroaspis polylepis, nanofractionated venom.
DOI: 10.6084/m9.figshare.21081721
title: Sleep duration and visceral adipose tissue: linear and nonlinear Mendelian randomization analyses

description: Increasing evidence suggests that sleep is important for fat metabolism. However, the causal relationship between sleep duration and visceral adipose tissue (VAT) needs to be further clarified. This study investigated the linear and nonlinear causal association between sleep duration and VAT.
DOI: 10.6084/m9.figshare.21081721.v1
title: Sleep duration and visceral adipose tissue: linear and nonlinear Mendelian randomization analyses

description: Increasing evidence suggests that sleep is important for fat metabolism. However, the causal relationship between sleep duration and visceral adipose tissue (VAT) needs to be further clarified. This study investigated the linear and nonlinear causal association between sleep duration and VAT.
DOI: 10.6084/m9.figshare.21081781
title: Supplementary Table 1

description: <strong>Supplementary Table 1</strong>
DOI: 10.6084/m9.figshare.21081781.v1
title: Supplementary Table 1

description: <strong>Supplementary Table 1</strong>
DOI: 10.6084/m9.figshare.21081787
title: Supplementary Table 2

description: <strong>Supplementary Table 2</strong>
DOI: 10.6084/m9.figshare.21081787.v1
title: Supplementary Table 2

description: <strong>Supplementary Table 2</strong>
DOI: 10.6084/m9.figshare.21081790
title: Supplementary Table 3

description: <strong>Supplementary Table 3</strong>
DOI: 10.6084/m9.figshare.21081793
title: Supplementary Table 4

description: <strong>Supplementary Table 4</strong>
DOI: 10.6084/m9.figshare.21081793.v1
title: Supplementary Table 4

description: <strong>Supplementary Table 4</strong>
DOI: 10.34740/kaggle/dsv/4193708
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.21081796
title: Supplementary Table 6

description: <strong>Supplementary Table 6</strong>
DOI: 10.6084/m9.figshare.21081796.v1
title: Supplementary Table 6

description: <strong>Supplementary Table 6</strong>
DOI: 10.6084/m9.figshare.21081868
title: Supplementary Table 7

description: <strong>Supplementary Table 7</strong>
DOI: 10.6084/m9.figshare.21081868.v1
title: Supplementary Table 7

description: <strong>Supplementary Table 7</strong>
DOI: 10.6084/m9.figshare.21081871
title: Supplementary Table 8

description: <strong>Supplementary Table 8</strong>
DOI: 10.6084/m9.figshare.21081871.v1
title: Supplementary Table 8

description: <strong>Supplementary Table 8</strong>
DOI: 10.5281/zenodo.7071614
title: Data Argo floats Cant

description: Data of the Argo floats with the estimation of anthropogenic carbon
DOI: 10.5281/zenodo.7071613
title: Data Argo floats Cant

description: Data of the Argo floats with the estimation of anthropogenic carbon
DOI: 10.5281/zenodo.7071628
title: SciExpeM - 10.5281/zenodo.7071628

description: Cong, Tanh Le; Bedjanian, Emma; Dagaut, Philippe - Combustion Science And Technology, 2010, (182), 333-349
DOI: 10.5281/zenodo.7071627
title: SciExpeM - 10.5281/zenodo.7071628

description: Cong, Tanh Le; Bedjanian, Emma; Dagaut, Philippe - Combustion Science And Technology, 2010, (182), 333-349
DOI: 10.6084/m9.figshare.19593151
title: Dataset S1 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Name and position of all annotated lncRNAs in bed12 format with the biotype, CPATscore and confidence level
DOI: 10.6084/m9.figshare.19593151.v1
title: Dataset S1 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Name and position of all annotated lncRNAs in bed12 format with the biotype, CPATscore and confidence level
DOI: 10.6084/m9.figshare.21081895
title: Supplementary Table 5

description: <strong>Supplementary Table 5</strong>
DOI: 10.6084/m9.figshare.21081895.v1
title: Supplementary Table 5

description: <strong>Supplementary Table 5</strong>
DOI: 10.5281/zenodo.7071575
title: Data for "Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool"

description: Data on soil-carbon and soil-nitrogen. D. G. T. Naidu, S. Roy, S. Bagchi, Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool. <em>Proceedings of the National Academy of Sciences</em> <strong>in press</strong> (2022).
description: {"references": ["D. G. T. Naidu, S. Roy, S. Bagchi, Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool. Proceedings of the National Academy of Sciences in press (2022)."]}
DOI: 10.5281/zenodo.7071574
title: Data for "Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool"

description: Data on soil-carbon and soil-nitrogen. D. G. T. Naidu, S. Roy, S. Bagchi, Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool. <em>Proceedings of the National Academy of Sciences</em> <strong>in press</strong> (2022).
description: {"references": ["D. G. T. Naidu, S. Roy, S. Bagchi, Loss of grazing by large mammalian herbivores can destabilize the soil carbon pool. Proceedings of the National Academy of Sciences in press (2022)."]}
DOI: 10.6084/m9.figshare.19593199
title: Dataset S2 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Redundant lncRNAs biotypes in Bed12 format plus biotype in column number 13
DOI: 10.6084/m9.figshare.19593199.v1
title: Dataset S2 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Redundant lncRNAs biotypes in Bed12 format plus biotype in column number 13
DOI: 10.6084/m9.figshare.21081898
title: Supplementary Table 9

description: <strong>Supplementary Table 9</strong>
DOI: 10.6084/m9.figshare.21081898.v1
title: Supplementary Table 9

description: <strong>Supplementary Table 9</strong>
DOI: 10.6084/m9.figshare.21081904
title: Supplementary Table 10

description: <strong>Supplementary Table 10</strong>
DOI: 10.6084/m9.figshare.21081904.v1
title: Supplementary Table 10

description: <strong>Supplementary Table 10</strong>
DOI: 10.6084/m9.figshare.21081910
title: Supplementary Table 11

description: <strong>Supplementary Table 11</strong>
DOI: 10.34740/kaggle/ds/1669409
title: Real-Time Pond Water Dataset for Fish Farming
DOI: 10.6073/pasta/0af328da4326460cc0dea1aa9cda5b5d
title: McMurdo Dry Valleys LTER: Average glacier stake heights and snow depths in the McMurdo Dry Valleys, Antarctica from 1993 to present

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes stake height and snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
DOI: 10.6073/pasta/3f0e21c6961046d381e1fae6b73a19d1
title: McMurdo Dry Valleys LTER: Daily measurement summaries from Explorers Cove Meteorological Station (EXEM) in Taylor Valley, Antarctica from 1997 to present

description: As part of the McMurdo Dry Valleys Long Term Ecological Research program, meteorological data are collected from various locations throughout the McMurdo Dry Valleys of Antarctica. This package contains daily measurement summaries derived from 15-minute data generated by the Explorer's Cove Meterological Station (EXEM), located in Taylor Valley (Latitude: -77.5887, Longitude: 163.4175, Elevation: 25 meters above sea level).Â EXEM was acquired from USGS in 1997, during the 97/98 field season.
DOI: 10.6073/pasta/be2f27d49d9e0289ffd64871a6e1f3d1
title: McMurdo Dry Valleys LTER: Daily measurement summaries from Lake Hoare Meteorological Station (HOEM) in Taylor Valley, Antarctica from 1987 to present

description: As part of the McMurdo Dry Valleys Long Term Ecological Research program, meteorological data are collected from various locations throughout the McMurdo Dry Valleys of Antarctica. This data packageÂ contains daily measurement summaries derived from 15-minute data generated by theÂ Lake Hoare Meteorological Station (HOEM), located in Taylor Valley.
DOI: 10.6073/pasta/1ac8da354bff946f8cb14eec393737f0
title: McMurdo Dry Valleys LTER: Daily measurement summaries from Lake Fryxell Meteorological Station (FRLM) in Taylor Valley, Antarctica from 1993 to present

description: As part of the McMurdo Dry Valleys Long Term Ecological Research program, meteorological data are collected from various locations throughout the McMurdo Dry Valleys of Antarctica. This data packageÂ contains daily measurement summaries derived from 15-minute data generated by the Lake Fryxell Meteorological Station (FRLM), located in Taylor Valley. BOYM was established in 1993, during the 93/94 field season.
DOI: 10.6073/pasta/3220523d0aab8489375a9997856ec1a9
title: McMurdo Dry Valleys LTER: Daily measurement summaries from Lake Bonney Meteorological Station (BOYM) in Taylor Valley, Antarctica from 1993 to present

description: As part of the McMurdo Dry Valleys Long Term Ecological Research program, meteorological data are collected from various locations throughout the McMurdo Dry Valleys of Antarctica. This packageÂ contains daily measurement summaries derived from 15-minute data generated by the Lake Bonney Meteorological Station (BOYM), located in Taylor Valley. BOYM was established in 1993, during the 93/94 field season.
DOI: 10.6084/m9.figshare.19420058
title: Genome assembly, annotation for rice bean

description: We present a high-quality reference genome for rice bean landrace FF25 based on PacBio long-read, Illumina short-read and HiC map.
DOI: 10.57745/u4icvq/qwhxgf
title: EP_QuestionsPostingWorkers.tab

description: :unav
DOI: 10.57745/u4icvq/yhowqg
title: readme.txt

description: :unav
DOI: 10.25678/000768
title: Data for: Are spray drift losses to agricultural roads more important for surface water contamination than direct drift to surface waters?

description: Spray drift is considered a major pesticide transport pathway to surface waters. Current research and legislation usually only considers direct spray drift. However, also spray drift on roads and subsequent wash-off to surface waters was identified as a possible transport pathway. Hydraulic shortcuts (storm drainage inlets, channel drains, ditches) have been shown to connect roads to surface waters, thus increasing the risk of drift wash-off to surface waters. However, the importance of this pathway has never been assessed on larger scales. To address this knowledge gap, we studied 26 agricultural catchments with a predominance of arable cropping (n = 17) and vineyards (n = 9). In these study sites, we assessed the occurrence of shortcuts by field mapping. Afterwards, we modelled the areas of roads drained to surface waters using a high-resolution digital elevation model (0.5 m resolution) and a multiple flow algorithm. Finally, we modelled drift deposition to drained roads and surface waters using a spatially explicit, georeferenced spray drift model. Our results show that for most sites, the drift to drained roads is much larger than the direct drift to surface waters. In arable land sites, drift to roads exceeds the direct drift by a factor of 4.5 to 18, and in vineyard sites by 35 to 140. In arable land sites, drift to drained roads is rather small (0.0015% to 0.0049% of applied amount) compared to typical total pesticide losses to surface waters. However, substantial drift to drained roads in vineyard sites was found (0.063% to 0.20% of applied amount). Current literature suggests that major fractions of the drift deposited on roads can be washed off during rain events, especially for pesticides with low soil adsorption coefficients. For such pesticides and particularly in vineyards, spray drift wash-off from drained roads is therefore expected to be a major transport pathway to surface waters.
DOI: 10.6084/m9.figshare.19593205
title: Dataset S3 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of metadata and ID of all transcriptomes used in this project
DOI: 10.6084/m9.figshare.19593205.v2
title: Dataset S3 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of metadata and ID of all transcriptomes used in this project
DOI: 10.6084/m9.figshare.19593205.v1
title: Dataset S3 of Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

description: Table of metadata and ID of all transcriptomes used in this project
DOI: 10.6084/m9.figshare.21081931
title: Raman fungi dataset

description: Single-cell Raman dataset of Raman spectra of 94 patients' isolate from various infection sources.
DOI: 10.21223/drwbk2/uq2lae
title: 01Regional_climatic_data_from_Rwanda.rar

description: :unav
DOI: 10.5281/zenodo.7065974
title: Experimental Datasets and Processing Codes for the Semantic PHD Filter

description: The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames <strong>1. Detection dataset: </strong> Size: <br> Total: 4870 images<br> Training: 4000 images<br> Validation: 870 images Bottle Classes: Aquafina, Deer, Kirkland, Nestle Format: PASCAL VOC, Darknet Folder Structure:<br> - Annotations: containing the xml label files in PASCAL VOC format<br> - ImageSets: containing the training index files <br> - JPEGImages: containing the image data in jpg format<br> - Labels: containing the txt label files in Darknet format <strong>2. Measurement model dataset:</strong> Format: ROSBAG Duration: 19:59s (1199s) Topics:<br> /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image<br> /map 1 msg : nav_msgs/OccupancyGrid<br> /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements<br> /tf 142727 msgs : tf2_msgs/TFMessage<br> /tf_static 1 msg : tf2_msgs/TFMessage Message Types:<br> nav_msgs/OccupancyGrid<br> sensor_msgs/Image<br> sphd_msgs/SPHDMeasurements<br> tf2_msgs/TFMessage <strong>3. Processing codes:</strong> Detection processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066045<br> GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector<br> <br> Measurement model processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066050<br> GitHub: https://github.com/TempleRAIL/sphd_sensor_models