cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.c.3621095_d9
title: Additional file 5: of The trade-off of availability and growth inhibition through copper for the production of copper-dependent enzymes by Pichia pastoris

description: Flux ratios and mass distribution vectors of amino acids. (XLS 192 kb)
DOI: 10.6084/m9.figshare.c.3621155_d1
title: Additional file 2: of Innate immunity based cancer immunotherapy: B16-F10 murine melanoma model

description: Fig5_DATA. (XLSX 151 kb)
DOI: 10.6084/m9.figshare.c.3621155_d2
title: Additional file 1: of Innate immunity based cancer immunotherapy: B16-F10 murine melanoma model

description: Fig1-4,6,7_DATA. (XLSX 45 kb)
DOI: 10.6084/m9.figshare.c.3621137_d10
title: Additional file 3:Â Dataset 1. of A critical role for the Drosophila dopamine D1-like receptor Dop1R2 at the onset of metamorphosis

description: Summary of microarray data. (XLSX 61 kb)
DOI: 10.6084/m9.figshare.c.3621167_d1
title: Additional file 4: of Gene expression profiling of trout regenerating muscle reveals common transcriptional signatures with hyperplastic growth zones of the post-embryonic myotome

description: Major functional categories of cluster III and lists of genes that formed them. (XLSX 62 kb)
DOI: 10.17863/cam.71937
title: Raw data for Conversion of polyethylene waste into gaseous hydrocarbons via integrated tandem chemical – photo/electrocatalytic processes

description: Raw data for manuscript 'Conversion of polyethylene waste into gaseous hydrocarbons via integrated tandem chemical – photo/electrocatalytic processes'. Includes GC and HPLC data - contains product analysis data for all photo- and electrocatalytic experiments discussed in Figures 1, 2, 3, S4, S16-17 and Tables S1, S3, S4-11, S13-S17 (Excel). Other Characterization - contains UV-Vis, XRD, O2 quantification, Cyclovoltamertry, NMR for Figure S2, S6, S7, S9, S13-15 and Table S12 (Origin, Excel, NMR raw data).
DOI: 10.17863/cam.31455
title: Research data supporting "Improving mechanical strength of YBCO bulk superconductors by Ag addition"

description: Research data associated with named publication. Includes microscope images, imageJ data, instron 3 point bend test data and porosity of YBCO-Ag, TSMG YBCO and LR TSMG YBCO
DOI: 10.13120/33aebefa-3554-4906-a484-cbcc18b2edca
title: North East Atlantic Ocean- Beach litter aggregated datasets 2001/2024 v2024
title: Atlantic_ocean_beach_litter_aggregated_datasets_ 2001/2024_v2024

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification, contaminants and litter. The chosen parameters are relevant for the Marine Strategy Framework Directive (MSFD), in particular for descriptors 5, 8, 9 and 10. The dataset contains standardized, harmonized and validated data collections from beach litter (monitoring and other sources). 
Datasets concerning beach and seafloor litter data are loaded in a central database after a semi-automated validation phase. Once loaded, a data assessment is performed in order to check data consistency and potential errors are corrected thanks to a feedback loop with data originators.
For beach litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on beach litter, including monitoring data, data from cleaning surveys and data from research. A relevant part of the monitoring data has been considered for assessment purposes by the European institutions and therefore is tagged as MSFD_monitoring. EMODnet beach litter data and databases are hosted and maintained by 'Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)' from Italy.
Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508. The updated vocabularies of admitted values are available in https://nodc.ogs.it/marinelitter/vocab.
The harmonized datasets can be downloaded as EMODnet Beach litter data format Version 7.0, which is a spreadsheet file composed of 4 sheets: beach metadata, survey metadata, animals and litter. Local_CDI field in the survey metadata sheet allows to retrieve the original data.
DOI: 10.17863/cam.44570
title: Research data supporting "Inhibiting Analyte Theft in SERS Substrates: sub-Nanomolar Quantitative Drug Detection"

description: Data was collected using a Renishaw Invia Raman setup through a 5x objective. Majority of data was taken at 785nm unless stated otherwise. All data is in txt format.
DOI: 10.25423/cmcc/blksea_analysisforecast_phy_007_001_eas6
title: Black Sea Physical Analysis and Forecast (Copernicus Marine Service BLK-PHY, EAS6 system)
title: BLKSEA_ANALYSISFORECAST_PHY_007_001

description: The Copernicus Marine Black Sea Physics Analysis and Forecast product is based on the NEMO v4.2.1 ocean circulation model, online coupled with WaveWatch III v6.0.7. It uses a variational data assimilation scheme called OceanVar, able to assimilate temperature and salinity vertical profiles, satellite SLA along track data and satellite SST data from Copernicus Marine associated TACs. The model has a 1/40˚ regular horizontal grid resolution and 121 unevenly spaced vertical levels. The model implements open boundary conditions at the Marmara Sea box for the optimal interface with the Mediterranean Sea by using high resolution model solutions provided by the Unstructured Turkish Strait System (U-TSS) model. Results from the U-TSS model for the Marmara Sea are interpolated onto a 1/160° regular horizontal grid and provided as additional datasets within the Black Sea product.
DOI: 10.5281/zenodo.8109281
title: Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment

description: This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify) also contains examples of how to use these models and related Python code.


The following connected paper is currently under review and should be cited together with these model versions: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)


Note on the file formats and notation:


"Primary data" refers to the .ptu files, so the actual TCSPC data. "Secondary data" refers to the .pqres files, which are derived files by the proprietary PicoQuant software.


Note on sample preparation (from the Supplementary Note of the paper above):


The peak artifacts measurements were produced by 20 nM Trypanosoma brucei-PEX5 N-term fused to eGFP in solution, and the corresponding control measurements by 5 nM Homo sapiens-PEX5 N-term fused to eGFP in solution. The detailed sample preparation is described elsewhere. We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 20 s for PEX5 experiments.
DOI: 10.5281/zenodo.8109282
title: Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment

description: This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify) also contains examples of how to use these models and related Python code.


The following connected paper is currently under review and should be cited together with these model versions: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)


Note on the file formats and notation:


"Primary data" refers to the .ptu files, so the actual TCSPC data. "Secondary data" refers to the .pqres files, which are derived files by the proprietary PicoQuant software.


Note on sample preparation (from the Supplementary Note of the paper above):


The peak artifacts measurements were produced by 20 nM Trypanosoma brucei-PEX5 N-term fused to eGFP in solution, and the corresponding control measurements by 5 nM Homo sapiens-PEX5 N-term fused to eGFP in solution. The detailed sample preparation is described elsewhere. We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 20 s for PEX5 experiments.
DOI: 10.17863/cam.8305
title: Research data supporting the publication 'Nonlinear Spectral Image Fusion'

description: This is Matlab code for the creation of image fusions based on the nonlinear spectral TV transform. The method of spectral image fusion is explained in the corresponding SSVM publication 'Nonlinear Spectral Image Fusion'. In order to run the automatic image fusion pipeline with the Obama/Reagan example as visualised in the paper, please follow the instructions in the readme.txt file in the folder 'spectralImageFusionOfFaces'. If you want to compute the spectral image fusions of Gauß and Newton in the supplementary files, please follow the instructions in the Matlab live scripts 'gaussnewton.mlx' or 'newtongauss.mlx' in the folder 'Banknote examples'.
DOI: 10.6084/m9.figshare.c.3621167_d8
title: Additional file 1: of Gene expression profiling of trout regenerating muscle reveals common transcriptional signatures with hyperplastic growth zones of the post-embryonic myotome

description: Heat map file for Java treeview visualisation of unsupervised clustering of differentially expressed genes during regeneration. (CDT 3818 kb)
DOI: 10.6084/m9.figshare.c.3621167_d9
title: Additional file 3: of Gene expression profiling of trout regenerating muscle reveals common transcriptional signatures with hyperplastic growth zones of the post-embryonic myotome

description: Major functional categories of cluster II and lists of genes that formed them. (XLSX 15 kb)
DOI: 10.17863/cam.102134
title: Research Data supporting "Sex-specific effects of a maternal obesogenic diet high in fat and sugar on offspring adiposity, growth and behaviour"

description: This study investigates the effect of maternal diet-induced obesity in mice on maternal and offspring (male and female) nutrient intake, growth and adiposity, and investigates the effects of maternal diet on the behaviour of male and female offspring in four tasks. Figure 1 describes the nutrient intake of pre-pregnant, pregnant and lactating mice on a control versus a high fat and high sugar (HFHS) diet (mean and SD across cages). Figure 2 describes the body weight (mean and SD), the fat mass (individual mice) and lean mass (individual mice) of pre-pregnant, pregnant and lactating mice on a control versus a HFHS diet. Figure 3 describes the body mass and fractional growth rate of the male and female pre-weaning offspring (2 to 21 days of age) of mothers on the control versus HFHS diet. Figure 4 describes the nutrient intake of the male and female offspring, after weaning (21 to 91 days of age) of mothers on the control versus HFHS diet. Figure 5 describes the body weight (mean and SD), fractional growth rate (mean and SD) and fat & lean masses (individual mice) in the male and female offspring, after weaning (21 to 91 days of age) of mothers on the control versus HFHS diet. Figure 6 describes 4 measures of anxiety-related behaviour in the elevated plus maze by male and female offspring of mothers on the control versus HFHS diet. Figure 7 describes 4 measures of cognitive behaviour in the novel object recognition task by male and female offspring of mothers on the control versus HFHS diet. Figure Supplementary 2 describes 4 measurements of locomotion and anxiety-related behaviour int he open field task by male and female offspring of mothers on the control versus HFHS diet. Figure Supplementary 3 describes 2 measures of social preference behaviour of male and female offspring of mothers on the control versus HFHS diet. Table 1 gives all of the body weights, organ weights and fat deposit weights measured in this study in the pre-pregnant, pregnant and lactating female mice and int he male and female offspring.
DOI: 10.6084/m9.figshare.c.3621200_d1
title: Additional file 3: of Ino80 is essential for proximal-distal axis asymmetry in part by regulating Bmp4 expression

description: Genotyping results from intercross of heterozygous Ino80 KO mice. Litters of mice or embryos at specified stages were collected and genotyped using the PCR-based genotyping strategy. Significant deviations from the expected genotype ratios were identified using a Chi-Square test (**pâ
DOI: 10.6084/m9.figshare.c.3621200_d2
title: Additional file 8: of Ino80 is essential for proximal-distal axis asymmetry in part by regulating Bmp4 expression

description: Supporting data. (XLSX 36 kb)
DOI: 10.6084/m9.figshare.c.3621203_d1
title: Additional file 1. of Diet-related chronic disease in the northeastern United States: a model-based clustering approach

description: Clustering results for the 129 counties disaggregated by state. Fewer clusters are found, however the overall cluster descriptions, as characterized by the NCHS codes and clustering variables are consistent with the aggregate clusters results presented.
DOI: 10.6084/m9.figshare.c.3621209_d1
title: Additional file 1: Table S1. of Complete genome sequence of an agr-dysfunctional variant of the ST239 lineage of the methicillin-resistant Staphylococcus aureus strain GV69 from Brazil

description: InDels into homopolymeric regions of the GV69 complete genome sequence in comparison with TW20 complete genome. (XLS 48 kb)
DOI: 10.6084/m9.figshare.c.3621215_d1
title: Additional file 2: of Genome-wide association study of copy number variation with lung function identifies a novel signal of association near BANP for forced vital capacity

description: Copy number class frequencies and lung function association results (FEV1, FVC and FEV1/FVC) for all 1962 CNVRs that were tested. (XLSX 1019 kb)
DOI: 10.6084/m9.figshare.c.3621206_d2
title: Additional file 1: Table S1. of Drought stress tolerance strategies revealed by RNA-Seq in two sorghum genotypes with contrasting WUE

description: Singular enrichment analysis using AgriGO to identify enriched gene ontologies among the 340 unique up-regulated genes in IS22330 under WW conditions. Table S2. Singular enrichment analysis using AgriGO to identify enriched gene ontologies among the 160 unique down-regulated genes in IS22330 under DS conditions. Table S3. Singular enrichment analysis using AgriGO to identify enriched gene ontologies among the 428 unique up-regulated genes resulted from the comparison of the two genotypes under DS conditions. Table S4. Singular enrichment analysis using AgriGO to identify enriched gene ontologies among the 393 unique down-regulated genes resulted from the comparison of the two genotypes under DS conditions. Table S5. Singular enrichment analysis using AgriGO to identify enriched gene ontologies among the 171 unique DEGs in response to DS conditions in both the genotypes. Table S6. Cross-comparison of enriched GO terms resulted from the unique up-regulated genes of IS20351 and IS22330 in response to DS conditions. Table S7. Cross-comparison of enriched GO terms resulted from the unique down-regulated genes of IS20351 and IS22330 in response to DS conditions. (XLSX 53 kb)
DOI: 10.6084/m9.figshare.c.3621200_d4
title: Additional file 6: of Ino80 is essential for proximal-distal axis asymmetry in part by regulating Bmp4 expression

description: Primers used in this Study. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.c.3621221_d1
title: MOESM1 of Bitpacking techniques for indexing genomes: II.Â Enhanced suffix arrays

description: Additional file 1. Source code for methods: Source code in an archive format, using tar and bzip2, for all implementations of enhanced suffix arrays, based on a modification of the SDSL 2.0 package. Package also includes benchmarking programs used for timing experiments. This is the same package as provided in our companion paper on bitpacking for hash tables.
DOI: 10.5281/zenodo.8082557
title: Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - AlexaFluor 488 applied experiment

description: This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify]) also contains examples of how to use these models and related Python code.


The following connected paper is currently under review and should be cited together with these model versions: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)


Note on the file formats and notation:


"Primary data" refers to the .ptu files, so the actual TCSPC data. "Secondary data" refers to the .pqres files, which are derived files by the proprietary PicoQuant software.


Note on sample preparation (from the Supplementary Note of the paper above):


The peak artifact measurements were produced by mixing 20 nM of the small dye Alexa Fluor 488 (Thermo Fisher Scientific) with 10 μM of slow, large (100 nm) unilamellar vesicles (LUVs) labelled with DiO in the membrane (0.1 % mol lipid:dye). The corresponding control measurements were 20 nM Alexa Fluor 488 in solution. We prepared the LUVs by the extrusion method: 1−palmitoyl−2−oleoyl−glycero−3−phosphocholine (POPC, Avanti Polar Lipids) was vacuum dried on a glass vial for 1 h and resuspended on phosphate buffered saline to form lipid vesicles. The vesicles were extruded 30 times through 100 nm pore size polycarbonate membranes using a Mini-Extruder (Avanti Polar Lipids). We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 10 s for AF488 experiments.
DOI: 10.5281/zenodo.8082558
title: Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - AlexaFluor 488 applied experiment

description: This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify]) also contains examples of how to use these models and related Python code.


The following connected paper is currently under review and should be cited together with these model versions: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)


Note on the file formats and notation:


"Primary data" refers to the .ptu files, so the actual TCSPC data. "Secondary data" refers to the .pqres files, which are derived files by the proprietary PicoQuant software.


Note on sample preparation (from the Supplementary Note of the paper above):


The peak artifact measurements were produced by mixing 20 nM of the small dye Alexa Fluor 488 (Thermo Fisher Scientific) with 10 μM of slow, large (100 nm) unilamellar vesicles (LUVs) labelled with DiO in the membrane (0.1 % mol lipid:dye). The corresponding control measurements were 20 nM Alexa Fluor 488 in solution. We prepared the LUVs by the extrusion method: 1−palmitoyl−2−oleoyl−glycero−3−phosphocholine (POPC, Avanti Polar Lipids) was vacuum dried on a glass vial for 1 h and resuspended on phosphate buffered saline to form lipid vesicles. The vesicles were extruded 30 times through 100 nm pore size polycarbonate membranes using a Mini-Extruder (Avanti Polar Lipids). We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 10 s for AF488 experiments.
DOI: 10.17863/cam.37838
title: Data supporting "Layer formation and relaminarisation in plane Couette flow with spanwise stratification"

description: Data and gnuplot script required to construct figures 1-11.
DOI: 10.15468/dl.8azwt4
title: Occurrence Download

description: A dataset containing 236573 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Living Specimen)",
 "TaxonKey is Culex pipiens Linnaeus, 1758"
 ]
}

 The dataset includes 236573 records from 229 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033203-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gelsgy
title: Occurrence Download

description: A dataset containing 4254 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Natural Resources Wales (NRW) Licence Return Dataset"
 ]
}

 The dataset includes 4254 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000467-171107215919691/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gpj3qm
title: Occurrence Download

description: A dataset containing 5740 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhinella marina (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-137.8125 51.179342,-137.8125 -59.534318,-28.125 -59.534318,-28.125 51.179342,-137.8125 51.179342))"
 ]
}

 The dataset includes 5740 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000468-150204170955391/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.