cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3756211
title: A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods

description: Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of: 51 converted mass spectral files in mzML format An .RData-file containing the extracted ion chromtograms (EICs) The randomly selected subset and the original output table of MZmine in .csv-format Example .xlsx files for the classification 2 central classification tables Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).
description: {"references": ["https://doi.org/10.1021/ac301482k", "https://doi.org/10.1186/1471-2105-11-395", "https://doi.org/10.1186/1471-2105-9-375", "https://doi.org/10.1016/j.scitotenv.2020.138388", "https://doi.org/10.1007/s00216-015-8681-7", "https://www.ebi.ac.uk/metabolights/MTBLS1455"]}
DOI: 10.5281/zenodo.3519407
title: A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods

description: Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of: 51 converted mass spectral files in mzML format An .RData-file containing the extracted ion chromtograms (EICs) The randomly selected subset and the original output table of MZmine in .csv-format Example .xlsx files for the classification 2 central classification tables Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).
description: {"references": ["https://doi.org/10.1021/ac301482k", "https://doi.org/10.1186/1471-2105-11-395", "https://doi.org/10.1186/1471-2105-9-375", "https://doi.org/10.1016/j.scitotenv.2020.138388", "https://doi.org/10.1007/s00216-015-8681-7", "https://www.ebi.ac.uk/metabolights/MTBLS1455"]}
DOI: 10.5281/zenodo.6137081
title: Predicted and experimental chemical and ecotoxicological properties for the toxic unit based hazard assessment

description: <strong>Description</strong> This dataset contains ecotoxicity data of 1585 chemicals of environmental concern (CECs) and chemical identifiers. The ecotoxicity data was retrieved from US EPA ECOTOX Knowlegdebase in ASCII file format and was aggregated for the ecotoxicity groups algae, crustaceans, and fish based on the ideas of Busch et al. 2016. The dataset includes the 5-percentile, the mean and the geomean of all retrieved ecotoxicity for each compound. Missing ecotoxicity data was estimated with ECOSAR 1.0 algorithms for green algae, daphnids, and fish using ChemProp 6.8. The main purpose of this dataset is the toxic unit (TU) based hazard assessment of environmental water samples. Chemical properties were estimated using OPERA 2.7, Instant JChem, and ACD Percepta 2015 based on QSAR-ready SMILES derived from OPERA 2.7. All data aggregated from EcoTox Knowledgebase (e.g., raw values, species, etc.) is available in the dataset in the detailed sheets. REcoTox, the processing script written in R is available on GitHub. <strong>CAUTION</strong> It needs to be emphasized that quantitative-structure activity relationship data is just an estimate, which does not necessarily reflect the real property and behaviour of a modelled compound. The calculated data needs to be reviewed in deep. Especially for non-polar or very polar compounds, the QSAR predictions might fail. If a compound ranks high in the TU ranking, it is required to search for literature or regulative data evidences to underpin the finding to avoid false positive prioritizations. <strong>RELEASE NOTE</strong> Version 210714_v1 was created with REcoTox version v0.1.0.
DOI: 10.5281/zenodo.7045832
title: Complex plant quality - microbiota - population interactions modulate the response of a specialist herbivore to the defense of its host plant.

description: 1. Many specialist herbivores have evolved strategies to cope with plant defenses, with gut microbiota potentially participating to such adaptations. 2. In this study we assessed whether the history of plant use (population origin) and microbiota may interact with plant defense adaptation. 3. We tested whether microbiota enhance the performance of <em>Melitaea cinxia </em>larvae on their host plant, <em>Plantago lanceolata</em> and increase their ability to cope the defensive compounds, iridoid glycosides (IGs). 3. The gut microbiota was significantly affected by both larval population origin and host plant IG level. Contrary to our prediction, impoverishing the microbiota with antibiotic treatment did not reduce larval performance. 5. As expected for this specialized insect herbivore, sequestration of one of IGs was higher in larvae fed with plants producing higher concentration of IGs. These larvae also showed metabolic signature of intoxication (<em>i.e. </em>decrease in Lysine levels). However, intoxication on highly defended plants was only observed when larvae with history of poorly defended plants were simultaneously treated with antibiotics. 6. Our results suggest that both adaptation and microbiota contribute to the metabolic response of herbivores to plant defense though complex interactions.
DOI: 10.5281/zenodo.7045831
title: Complex plant quality - microbiota - population interactions modulate the response of a specialist herbivore to the defense of its host plant.

description: 1. Many specialist herbivores have evolved strategies to cope with plant defenses, with gut microbiota potentially participating to such adaptations. 2. In this study we assessed whether the history of plant use (population origin) and microbiota may interact with plant defense adaptation. 3. We tested whether microbiota enhance the performance of <em>Melitaea cinxia </em>larvae on their host plant, <em>Plantago lanceolata</em> and increase their ability to cope the defensive compounds, iridoid glycosides (IGs). 3. The gut microbiota was significantly affected by both larval population origin and host plant IG level. Contrary to our prediction, impoverishing the microbiota with antibiotic treatment did not reduce larval performance. 5. As expected for this specialized insect herbivore, sequestration of one of IGs was higher in larvae fed with plants producing higher concentration of IGs. These larvae also showed metabolic signature of intoxication (<em>i.e. </em>decrease in Lysine levels). However, intoxication on highly defended plants was only observed when larvae with history of poorly defended plants were simultaneously treated with antibiotics. 6. Our results suggest that both adaptation and microbiota contribute to the metabolic response of herbivores to plant defense though complex interactions.
DOI: 10.34740/kaggle/dsv/4155375
title: YouTube Daily
DOI: 10.6084/m9.figshare.20301198
title: Onion_53ID_5011SNP.vcf

description: Filtered SNP variants of 53 onion accessions in VCF v4.0 format. For details on SNP calling and filtering, please see M&M of the manuscript titled: "<strong>Genotyping by sequencing defines genetic structure within the “Acquaviva” red onion landrace</strong>".
DOI: 10.6084/m9.figshare.20301198.v1
title: Onion_53ID_5011SNP.vcf

description: Filtered SNP variants of 53 onion accessions in VCF v4.0 format. For details on SNP calling and filtering, please see M&M of the manuscript titled: "<strong>Genotyping by sequencing defines genetic structure within the “Acquaviva” red onion landrace</strong>".
DOI: 10.5517/ccdc.csd.cc28r002
title: CCDC 2107473: Experimental Crystal Structure Determination

description: Related Article: Hongqun Shen|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34943/8096419f-a207-4ce5-a809-c5c3b763d425
title: St. John's Bay Wind Monitoring System Deployed 2022-08-17

description: The WindSonic SN 14520030 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Wind Monitoring System. Wind monitoring systems measure the speed and direction of wind. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/d28db8c2-b56e-417a-813f-b44b82fe275b
title: St. John's Bay Wind Monitoring System Deployed 2022-08-17

description: The WindSonic SN 09030012 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Wind Monitoring System. Wind monitoring systems measure the speed and direction of wind. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/4442b2c0-9ecd-44be-adfd-dcdf8da0b835
title: St. John's Bay Air Temperature and Humidity Sensor Deployed 2022-08-17

description: The Rotronic Meteorological Probe MP101A-T7 61631977 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Air Temperature and Humidity Sensor. Air temperature and humidity sensors are used to monitor weather conditions above the surface of the water. Air temperature affects wind speed and direction, relative humidity, the rate of evaporation and precipitation in the atmosphere. Air temperature is measured in numerous ways including thermistors, thermocouples and mercury thermometers. Humidity is the amount of water vapour in the air and can indicate precipitation, dew or fog. Humidity is typically measured as absolute humidity (the water content of air), relative humidity (measures the current absolute humidity relative to maximum for that temperature), or specific humidity (as a ratio of water vapour content to the total air content based on a mass). It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/d819bcf5-4fef-4cb5-9186-a11d0e7fb67c
title: St. John's Bay Datalogger Deployed 2022-08-17

description: The AXYS WatchMan Data Logger WM503440 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Datalogger. Data loggers, or recorder devices, are electronic devices that record data over time, or in relation to location, either with a built-in instrument or sensor or via external instruments and sensors, and are programmed with appropriate recording parameters. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/3111b6bb-42f7-49fc-a43d-92b03ecf01f4
title: St. John's Bay Navigation System Deployed 2022-08-17

description: The Garmin 19X HVS GPS 2J5059603 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Navigation System. Navigational instruments are used to measure and record positional information of ships, buoys, and autonomous underwater vehicles (AUVs). Typical sensors include latitude, longitude, speed, heading, pitch, roll, and depth. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/fda141a4-37cf-450f-98b2-771e235f43d1
title: St. John's Bay Wave Buoy Deployed 2022-08-17

description: The TRIAXYS Wave Buoy TAS03150 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Wave Buoy. Wave buoys provide measurements of wave conditions. Measurements include wave height, period, direction, and spread. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.7046085
title: drug fact boxes/p aubertin et al. 2022

description: The submitted dataset contains data for the comparison of different representations of frequencies in fact boxes.
DOI: 10.5281/zenodo.7046084
title: drug fact boxes/p aubertin et al. 2022

description: The submitted dataset contains data for the comparison of different representations of frequencies in fact boxes.
DOI: 10.34943/bdb0e6f9-a4b9-41b8-bd8f-728c0f4ad0c6
title: St. John's Bay Barometric Pressure Sensor Deployed 2022-08-17

description: The Vaisala PTB110 Barometer F4520055 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Barometric Pressure Sensor. Barometric pressure sensors are used to monitor weather fluctuations and conditions above the surface of the water. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6084/m9.figshare.20852179
title: Data from "Is torpor a water conservation strategy? Heterothermic responses to acute water and food deprivation are repeatable among individuals of Phodopus sungorus" J Therm Biol, Noakes et al. 2022

description: Data of the thermoregulatory responses of Siberian hamsters <em>Phodopus sungorus</em> to food and/or water deprivation. <br> Full details of methods and data analyses are provided in the manuscript: "Is torpor a water conservation strategy? Heterothermic responses to acute water and food deprivation are repeatable among individuals of Phodopus sungorus" Journal of Thermal Biology Noakes, M.J., Przybylska-Piecha, A.S., Wojciechowski, M.S. and Jefimow, M. 2022
DOI: 10.6084/m9.figshare.19690459.v3
title: Jiaozhou Bay 16S rRNA & metagenome dataset

description: Data for contigs, MAGs, and other files generated using the JZB microbial dataset. Dataset includes: <br> Single assembly contigs (10 files)： JZB_A53_27_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 2.7 μm pore-sized filter at A53 station in September 2015 in Jiazhou Bay. JZB_A53_07_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.7 μm pore-sized filter at A53 station in September 2015 in Jiazhou Bay. JZB_A53_02_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.22 μm pore-sized filter at A53 station in September 2015 in Jiazhou Bay. JZB_A53_27_201603_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 2.7 μm pore-sized filter at A53 station in March 2016 in Jiazhou Bay. JZB_A53_02_201603_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.22 μm pore-sized filter at A53 station in March 2016 in Jiazhou Bay. JZB_D710_27_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 2.7 μm pore-sized filter at D710 station in September 2015 in the open Yellow Sea. JZB_D710_07_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.7 μm pore-sized filter at D710 station in September 2015 in the open Yellow Sea. JZB_D710_02_201509_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.22 μm pore-sized filter at D710 station in September 2015 in the open Yellow Sea. JZB_D710_07_201603_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.7 μm pore-sized filter at D710 station in March 2016 in the open Yellow Sea. JZB_D710_02_201603_single_assembly.final.contigs.tar.gz: Contigs from the individual assembly of sample collected onto 0.22 μm pore-sized filter at D710 station in March 2016 in the open Yellow Sea. <br> Co-assembly contigs (3 files)： JZB_27_fractional_assembly.final.contigs.tar.gz: Contigs from co-assembly of samples collected onto 2.7 μm pore-sized filter. JZB_07_fractional_assembly.final.contigs.tar.gz: Contigs from co-assembly of samples collected onto 0.7 μm pore-sized filter. JZB_02_fractional_assembly.final.contigs.tar.gz: Contigs from co-assembly of samples collected onto 0.22 μm pore-sized filter. <br> JZB_MAGs.tar.gz: 915 non-redundant and purified prokaryotic genomes with ≥ 50% completeness and ≤ 5% contamination <br> Supplementary Table 1: Sampling information and environment factors. <br> Supplementary Table 2: The ASVs-table, ASVs taxonomy, and sequencing data information of 16S rDNA sequencing. <br> Supplementary Table 3: The contigs information; MAG information; reference genomes information of archaeal tree; reference genomes information of bacterial tree; Metagenome raw data information. <br>
DOI: 10.15128/r1f4752g787
title: Preparation of 87Rb and 133Cs in the motional ground state of a single optical tweezer [dataset]

description: We report simultaneous Raman sideband cooling of a single $^{87}$Rb atom and a single $^{133}$Cs atom held in separate optical tweezers at 814\,nm and 938\,nm, respectively. \slc{Starting from outside the Lamb-Dicke regime, after 45\,ms of cooling we measure probabilities to occupy the three-dimensional motional ground state of 0.86$^{+0.03}_{-0.04}$ for Rb and 0.95$^{+0.03}_{-0.04}$ for Cs.} Our setup overlaps the Raman laser beams used to cool Rb and Cs, reducing hardware requirements by sharing equipment along the same beam path. The cooling protocol is scalable, and we demonstrate cooling of single Rb atoms in an array of four tweezers. After motional ground-state cooling, a 938\,nm tweezer is translated to overlap with a 814\,nm tweezer so that a single Rb and a single Cs atom can be transferred into a common 1064\,nm trap. By minimising the heating during the merging and transfer, we prepare the atoms in the relative motional ground state with an efficiency of 0.81$^{+0.08}_{-0.08}$. This is a crucial step towards the association of arrays of single RbCs molecules confined in optical tweezers.
description: Raman sideband cooling of single atoms in optical tweezers
DOI: 10.6084/m9.figshare.7235471
title: HCL DGE Data

description: Single-cell analysis is a valuable tool to dissect cellular heterogeneity in complex systems. Yet, a systematic single-cell atlas has not been achieved for human beings. We used single-cell RNA sequencing to determine cell type composition of all major human organs and construct a basic scheme for the human cell landscape (HCL). We reveal a single-cell hierarchy for many tissues that has not been well characterized previously. We present a ‘‘single-cell HCL analysis’’ pipeline that accurately defines human cell types; and exemplify its utility in stem cell biology. Finally, we perform single-cell comparative analysis for human and mouse cell atlas to reveal the conserved genetic networks in the mammalian system.<b></b><br><b>File Extension Specification</b><b></b><b></b><br><b>HCL_Fig1_adata.h5ad</b>: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the data used for HCL Figure 1.<b></b><br><b>HCL_Fig1_cell_Info</b>: The information, which includes the cell names, samples, clusters, stages, batches, donors and cell types for cells of data used for HCL Figure 1.<br><b>cluster_markers_HCL&MCA1.1</b>: The cell type annotation and marker genes for 102 HCL clusters of Fig1 and 104 MCA1.1 clusters of SFig. <br><b><br></b><b>dge_raw_data.tar</b>: The raw digital expression matrix (dge) of more than 720,000 single cells sorted by tissues. The batch genes were not removed.<br><b>dge_rmbatch_data.tar</b>: The batch gene removed dge of more than 700,000 primary single cells sorted by tissues. Some tissues are not included due to relatively strong batch effects. This dataset can be used to make global tissue tSNE plot and do cross-tissue analysis.<br><b>annotation_rmbatch_data.tar</b>: The cell annotations, which include cluster ID, belonged tissues, age<b><i> (gestational age for fetal tissue)</i></b>, clusters and cell types for each rmbatch dge data.<br><b>annotation_cluster_info: <i>Modified</i></b> cell type annotation of each cluster in accord with ClusterID in annotation_rmbatch_data.zip<br><b>MCA1.1_adata.h5ad</b>: Use scanpy.api.read_h5ad to load AnnData. This AnnData stores the MCA1.1 data. <br><b>MCA1.1_cell_Info</b>: The information, which includes the cell names, samples, clusters, stages, batches, donors and ce;; types for cells of MCA1.1 data.<br><br>