cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5bt65
title: MassIVE Reanalysis RMSV000000010.2 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000080004 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5fm8s
title: MassIVE Reanalysis RMSV000000020.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000079097 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5k999
title: MassIVE Reanalysis RMSV000000018.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000079942 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5mb0j
title: MassIVE Reanalysis RMSV000000008.2 - Original iPRG 2012 Participant Identification Results
title: iPRG 2012 participants converted to be compatible for MassIVE Reanalysis.
DOI: 10.25345/c5q425
title: MassIVE Reanalysis RMSV000000017.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000079943 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5tt54
title: MassIVE Reanalysis RMSV000000016.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000080269 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5zm72
title: MassIVE Reanalysis RMSV000000015.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000079947 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5299n
title: MassIVE Reanalysis RMSV000000023.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000079983 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5642h
title: MassIVE Reanalysis RMSV000000022.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000080265 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c59t43
title: MassIVE Reanalysis RMSV000000021.3 - MSGF+ 1% FDR - 50 ppm precursor mass tolerance
title: Automated MSGF-PLUS-AMBIGUITY reanalysis of dataset MSV000080049 at 1% FDR with 50 ppm precursor mass tolerance.
DOI: 10.25345/c5jx7f
title: MassIVE MSV000085702 - Optimization of digestion buffer for middle-down proteomics of histone extracts from tilapia kidney
title: Experiments were performed by Elizabeth Mojica in the laboratory of Dietmar Kueltz at UC Davis. Sample preparation was based on histone acid extraction combined with a modification of a previously published protocol (Kultz et al., 2013, MCP 12/12, 3962-3975). Four different V8 protease digestion buffers were tested. Biological mass spectrometry (Middle-down proteomics) was performed in the Kueltz laboratory. Samples represent a single biological replicate processed using an identical procedure, except for the composition of the V8 protease digestion buffer. Each of the four V8 digestion buffer conditions are represented by three samples, one of which was acquired using regular bottom-up parameters while the others were acquired by adjusting parameters for middle-down proteomics. Sample concentrations were different between acquisitions.
DOI: 10.25345/c51x7s
title: MassIVE MSV000085706 - Consequences of aneuploidy in human fibroblasts
title: To characterize aneuploidy driven phenotypes in human trisomies, we performed global transcriptome, proteome, and phenotypic analysis of primary human fibroblasts from individuals with Patau (trisomy 13), Edwards (trisomy 18), or Down syndromes.
DOI: 10.25345/c55r11
title: MassIVE MSV000085705 - Soderling Astrocyte Split-BioID
title: Sample Preparation. The Duke Proteomics and Metabolomics Shared Resource received 16 samples (4 replicates each of Split-TurboID, NT or CT, TurboID-GPI, Soluble TurboID). Samples were spiked with either a total of 120 or 240 fmol of casein and reduced with 10 mM dithiolthreitol for 30 min at 80C and alkylated with 20 mM iodoacetamide for 45 min at room temperature, then supplemented with a final concentration of 1.2 phosphoric acid and 328 ul of S-Trap (Protifi) binding buffer (90 MeOH/100mM TEAB). Proteins were trapped on the S-Trap, digested using 20 ng/ul sequencing grade trypsin (Promega) for 1 hr at 47C, and eluted using 50 mM TEAB, followed by 0.2 FA, and lastly using 50 ACN/0.2 FA. All samples were then lyophilized to dryness and resuspended in 12 uL 1 TFA/2 acetonitrile containing 12.5 fmol/uL yeast alcohol dehydrogenase (ADH_YEAST). From each sample, 3 uL was removed to create a QC Pool sample which was run periodically throughout the acquisition period.
DOI: 10.25345/c59f15
title: MassIVE MSV000085704 - Soderling Astrocyte Split BioID
title: Sample Preparation. The Duke Proteomics and Metabolomics Shared Resource received 16 samples (4 replicates each of Split-TurboID, NT or CT, TurboID-GPI, Soluble TurboID). Samples were spiked with either a total of 120 or 240 fmol of casein and reduced with 10 mM dithiolthreitol for 30 min at 80C and alkylated with 20 mM iodoacetamide for 45 min at room temperature, then supplemented with a final concentration of 1.2 phosphoric acid and 328 ul of S-Trap (Protifi) binding buffer (90 MeOH/100mM TEAB). Proteins were trapped on the S-Trap, digested using 20 ng/ul sequencing grade trypsin (Promega) for 1 hr at 47C, and eluted using 50 mM TEAB, followed by 0.2 FA, and lastly using 50 ACN/0.2 FA. All samples were then lyophilized to dryness and resuspended in 12 uL 1 TFA/2 acetonitrile containing 12.5 fmol/uL yeast alcohol dehydrogenase (ADH_YEAST). From each sample, 3 uL was removed to create a QC Pool sample which was run periodically throughout the acquisition period.
DOI: 10.25345/c5f74g
title: MassIVE MSV000085703 - Multi-Omic Analysis of COVID-19 Patient Cohort
title: Prospective cohort study involving 128 patients with and without COVID-19 diagnosis. Blood samples collected from the patients were separated into leukocytes for RNA sequencing or plasma for proteomic, metabolomic, and lipidomic analyses. The four omic datasets were then correlated with disease severity and outcomes. Overall, our study contributes a multi-dimensional big data platform that can be used to gain important insight into the complexities of COVID-19, propose therapeutic targets, and facilitate accurate predictions of outcomes.
DOI: 10.25345/c53q93
title: MassIVE MSV000085713 - MetFish: A Metabolomics Platform for Studying Microbial Communities in Chemically Extreme Environments
title: Here we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exo-metabolomics analysis in sample matrices containing up to 2 molar total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and can be integrated into either targeted or untargeted analysis pipelines. MetFish was successfully applied in targeted and untargeted exo-metabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exo-metabolome during community succession; in situ in a native prairie soil, whose exo-metabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exo-metabolome over time in the well.
DOI: 10.25345/c57j3b
title: MassIVE MSV000085712 - Reanalysis of microglia data to demonstrate Peptide Correlation Analysis (PeCorA)
title: Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies, which are produced by cleavage of all isolated protein by a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false for biological or technical reasons. Here, I describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. Simple linear models are used to assess whether the slope of a peptide's change across treatment groups differs from the slope of all other peptides assigned to the same protein. Reanalysis of proteomic data from primary mouse microglia with PeCorA revealed that about 15% of proteins contain one discordant peptide. Inspection of the discordant peptides shows utility of PeCorA for direct and indirect detection of regulated PTMs, and also for discovery of poorly quantified peptides that should be excluded. PeCorA can be applied to an arbitrary list of quantified peptides, and is freely available as a script written in R. Data from microglia uploaded here was previously published by other authors https://doi.org/10.1016/j.jprot.2020.103753
DOI: 10.25345/c5c73g
title: MassIVE MSV000085711 - JA_July082020_HighResStrawsPFAS
title: pfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfaspfas
DOI: 10.25345/c5h16s
title: MassIVE MSV000085710 - Proteomics and phosphoproteomics of PDAC
title: Proteomics and phosphoproteomics analysis of PDAC.
DOI: 10.25345/c5mq9r
title: MassIVE MSV000085709 - Tau and other proteins found in Alzheimer's disease spinal fluid are linked to retromer-mediated endosomal traffic
title: Comparison of proteins from pooled CSF from 4 control mice and 5 VPS35 Camk2a KO mice
DOI: 10.25345/c5rj2m
title: MassIVE MSV000085708 - GNPS-OSMAC-218_066,F2&PDB_blank,PDB_PDA_rice_F2
title: The fungi 218_066 is studied for the profile of secondary metabolites by OSMAC strategy
DOI: 10.25345/c5x756
title: MassIVE MSV000085707 - Human and E. coli CV stepping with FAIMS for ion mobility prediction
title: Data from human and e coli peptides that were analyzed with a single FAIMS compensation voltage (CV) from -20 to -120. Data was used to train machine learning models to predict the FAIMS transmission profile of other peptides
DOI: 10.25345/c5017h
title: MassIVE MSV000085717 - Chemical footprinting of Polyhomeotic 1289-1577
title: The Polycomb protein Polyhomeotic (Ph) is implicated in organizing chromatin to regulate gene expression. This activity depends on the sterile alpha motif in Ph, which can form helical polymers. This polymerization activity is believed to create macromolecular assemblies of Polycomb proteins and chromatin in cells. To understand how this may occur, and the possible role of phase separation in the process, we analyzed a truncated version of Ph (aa1289-1577, Mini-Ph) in vitro. We find that Mini-Ph forms phase separated condensates with DNA or chromatin in vitro. To understand how Ph SAM polymerization contributes to this activity, and how Mini-Ph conformation might change on binding DNA and phase separating, we used a protein footprinting method based on chemical acetylation of accessible lysine residues. Mini-Ph or a polymerization mutant, alone, or with DNA, was treated with sulfo-NHS-acetate to acetylate accessible lysines. After protein denaturation, the protein was treated with propionic acid to propionylate unacetylated lysines. Samples were analyzed by MS, and the ratio of acetylated/propionylated lysines quantified at each position using MaxQuant.
DOI: 10.25345/c52r0q
title: MassIVE MSV000085724 - GNPS - U19 - HNRC Plasma HIV and Depression - Updated Project - ReDU
title: Plasma samples from HNRC for low biomass protocol testing. Data was acquired using a Bruker Maxis Impact and C18 RP-UHPLC. Positive polarity acquisition of LC-MS/MS. Updated files here.
DOI: 10.25345/c56j18
title: MassIVE MSV000085723 - Identification of the global histone posttranslational modification landscape in testes of euryhaline tilapia
title: Experiments were performed by Elizabeth Mojica in the laboratory of Dietmar K&uumlltz at UC Davis. Sample preparation was based on histone acid extraction combined with a previously published LCMS2 sample preparation protocol (https://doi.org/10.1074/mcp.M113.029827). Dataset 1 - EAM0008_Oremo-Gill_V8: In this experiment the V8 (Glu-C) protease digestion buffer for middle-down proteomics of histone extracts was optimized. Four different V8 protease digestion buffers were tested. Samples represent a single biological replicate processed using an identical procedure, except for the composition of the V8 protease digestion buffer. Each of the four V8 digestion buffer conditions are represented by three samples, one of which was acquired using regular bottom-up parameters while the others were acquired by adjusting parameters for middle-down proteomics (m/z range, collision energies, preferred charge states). Sample concentrations were different between acquisitions. Biological mass spectrometry (Middle-down proteomics) was performed and data analyzed using a workflow consisting of Compass Hystar 3.2, DataAnalysis 4.4, PEAKS Studio X+, Scaffold 4.11, and ScaffoldPTM 3.0 in the K&uumlltz laboratory.
DOI: 10.25345/c5b75x
title: MassIVE MSV000085722 - Identification of the global histone posttranslational modification landscape in gill epithelium of euryhaline tilapia
title: Experiments were performed by Elizabeth Mojica in the laboratory of Dietmar K&uumlltz at UC Davis. Sample preparation was based on histone acid extraction combined with a previously published LCMS2 sample preparation protocol (https://doi.org/10.1074/mcp.M113.029827). Dataset 1 - EAM0008_Oremo-Gill_V8: In this experiment the V8 (Glu-C) protease digestion buffer for middle-down proteomics of histone extracts was optimized. Four different V8 protease digestion buffers were tested. Samples represent a single biological replicate processed using an identical procedure, except for the composition of the V8 protease digestion buffer. Each of the four V8 digestion buffer conditions are represented by three samples, one of which was acquired using regular bottom-up parameters while the others were acquired by adjusting parameters for middle-down proteomics (m/z range, collision energies, preferred charge states). Sample concentrations were different between acquisitions. Biological mass spectrometry (Middle-down proteomics) was performed and data analyzed using a workflow consisting of Compass Hystar 3.2, DataAnalysis 4.4, PEAKS Studio X+, Scaffold 4.11, and ScaffoldPTM 3.0 in the K&uumlltz laboratory.
DOI: 10.25345/c5g16g
title: MassIVE MSV000085721 - Optimization of digestion buffer for middle-down proteomics of histone extracts from tilapia kidney
title: Experiments were performed by Elizabeth Mojica in the laboratory of Dietmar K&uumlltz at UC Davis. Four different V8 protease digestion buffers were tested. Sample preparation was based on histone acid extraction combined with a previously published protocol (https://doi.org/10.1074/mcp.M113.029827). Samples represent a single biological replicate processed using an identical procedure, except for the composition of the V8 protease digestion buffer. Each of the four V8 digestion buffer conditions are represented by three samples, one of which was acquired using regular bottom-up parameters while the others were acquired by adjusting parameters for middle-down proteomics (m/z range, collision energies, preferred charge states). Sample concentrations were different between acquisitions. Biological mass spectrometry (Middle-down proteomics) was performed and data analyzed using a workflow consisting of Compass Hystar 4.1, DataAnalysis 4.4, PEAKS Studio X+, Scaffold 4.11, and ScaffoldPTM3.0 in the K&uumlltz laboratory.
DOI: 10.25345/c5kr0c
title: MassIVE MSV000085720 - GNPS Example Dataset Sisal Sediments
title: This example is to know if our data was processed propertly. In in this experiment we want observe the chemical composition from diferent samples
DOI: 10.25345/c5qj29
title: MassIVE MSV000085719 - Optimization of digestion buffer for middle-down proteomics of histone extracts from tilapia gill
title: Experiments were performed by Elizabeth Mojica in the laboratory of Dietmar Kueltz at UC Davis. Sample preparation was based on histone acid extraction combined with a modification of a previously published protocol (Kultz et al., 2013, MCP 12/12, 3962-3975). Four different V8 protease digestion buffers were tested. Biological mass spectrometry (Middle-down proteomics) was performed in the Kueltz laboratory. Samples represent a single biological replicate processed using an identical procedure, except for the composition of the V8 protease digestion buffer. Each of the four V8 digestion buffer conditions are represented by three samples, one of which was acquired using regular bottom-up parameters while the others were acquired by adjusting parameters for middle-down proteomics. Sample concentrations were different between acquisitions.
DOI: 10.25345/c5v746
title: MassIVE MSV000085718 - Proteomic analysis of PDAC tissue.
title: Comprehensive proteomic analysis of PDAC tissue from patients.
DOI: 10.25345/c5z15f
title: MassIVE MSV000085726 - Global Phosphoproteome Analysis Using FAIMS on a Hybrid Orbitrap Mass Spectrometer
title: Here, we incorporated a recently commercialized aerodynamic high-field asymmetric waveform ion mobility spectrometry (FAIMS) device into the phosphoproteomic workflow. We characterized the effects of phosphorylation on the FAIMS separation, described optimized compensation voltage settings for unlabeled phosphopeptides, and demonstrated the advantages of FAIMS-enabled gas-phase fractionation. FAIMS single-shot analyses identified up to 31% more phosphorylation sites than control experiments without FAIMS. In comparison to liquid chromatography pre-fractionation, FAIMS experiments yielded similar or superior results when analyzing up to four discrete fractions. Although using FAIMS led to a modest reduction in the precision of quantitative measurements, data collected with FAIMS yielded a 44% increase in total reproducible measurements.
DOI: 10.15129/2f3bdb7a-a19c-4e6c-86d8-368da31a2355
title: Data for: "Quantification of Particle Size and Concentration Using In-line Techniques and Multivariate Analysis"

description: This dataset contains the data analysed in the manuscript "Quantification of Particle Size and Concentration Using In-line Techniques and Multivariate Analysis" and it contains the follwing subsets: - a set of volume weighted particle size distributions (PSD) extracted from Mastersizer; - individual particle characteristics from Morphologi G3 measurements; - a set of chord length distributions (CLD) from focus beam reflectance measurement (FBRM); - a set of in-line microscopy images analysed by particle vision measurement (PVM)* The particle size range analysed by in-line and off-line techniques was: &lt;90, 125-180, 180-250, 250-355, 300-500 and 630-800 um. The particle concentration ranged from 1 to 10 wt.% *The dataset available for download on this site is a sample of the complete dataset containing 100 images for each experimental condition. The complete dataset is freely available. However, due to its size, approximately 1 TB uncompressed, access should be requested using the contact email provided on this page.
DOI: 10.5517/ccdc.csd.cc23r9ry
title: CCDC 1958820: Experimental Crystal Structure Determination

description: Related Article: Weijie Zhu, Errui Li, Jiong Zhou, Yujuan Zhou, Xinru Sheng, Feihe Huang|2020|Mater. Chem. Front.|4|2325|doi:10.1039/D0QM00334D
DOI: 10.5517/ccdc.csd.cc23r9w2
title: CCDC 1958824: Experimental Crystal Structure Determination

description: Related Article: Weijie Zhu, Errui Li, Jiong Zhou, Yujuan Zhou, Xinru Sheng, Feihe Huang|2020|Mater. Chem. Front.|4|2325|doi:10.1039/D0QM00334D
DOI: 10.5517/ccdc.csd.cc23r9x3
title: CCDC 1958825: Experimental Crystal Structure Determination

description: Related Article: Weijie Zhu, Errui Li, Jiong Zhou, Yujuan Zhou, Xinru Sheng, Feihe Huang|2020|Mater. Chem. Front.|4|2325|doi:10.1039/D0QM00334D
DOI: 10.5517/ccdc.csd.cc24xf8s
title: CCDC 1993525: Experimental Crystal Structure Determination

description: Related Article: Takayuki Iwata, Mizuki Hyodo, Takuto Fukami, Yoshihito Shiota, Kazunari Yoshizawa, Mitsuru Shindo|2020|Chem.-Eur.J.|26|8506|doi:10.1002/chem.202002065
DOI: 10.5517/ccdc.csd.cc24xf9t
title: CCDC 1993526: Experimental Crystal Structure Determination

description: Related Article: Takayuki Iwata, Mizuki Hyodo, Takuto Fukami, Yoshihito Shiota, Kazunari Yoshizawa, Mitsuru Shindo|2020|Chem.-Eur.J.|26|8506|doi:10.1002/chem.202002065
DOI: 10.5517/ccdc.csd.cc24xfbv
title: CCDC 1993527: Experimental Crystal Structure Determination

description: Related Article: Takayuki Iwata, Mizuki Hyodo, Takuto Fukami, Yoshihito Shiota, Kazunari Yoshizawa, Mitsuru Shindo|2020|Chem.-Eur.J.|26|8506|doi:10.1002/chem.202002065
DOI: 10.5517/ccdc.csd.cc24xfcw
title: CCDC 1993528: Experimental Crystal Structure Determination

description: Related Article: Takayuki Iwata, Mizuki Hyodo, Takuto Fukami, Yoshihito Shiota, Kazunari Yoshizawa, Mitsuru Shindo|2020|Chem.-Eur.J.|26|8506|doi:10.1002/chem.202002065
DOI: 10.5517/ccdc.csd.cc24xfdx
title: CCDC 1993529: Experimental Crystal Structure Determination

description: Related Article: Takayuki Iwata, Mizuki Hyodo, Takuto Fukami, Yoshihito Shiota, Kazunari Yoshizawa, Mitsuru Shindo|2020|Chem.-Eur.J.|26|8506|doi:10.1002/chem.202002065
DOI: 10.5517/ccdc.csd.cc24xznq
title: CCDC 1994064: Experimental Crystal Structure Determination

description: Related Article: Dongliang Shi, Xiaoping Yang, Yanan Ma, Mengyu Niu, Richard A. Jones|2020|Chem.Commun.|56|8651|doi:10.1039/D0CC04242K
DOI: 10.5517/ccdc.csd.cc24xzpr
title: CCDC 1994065: Experimental Crystal Structure Determination

description: Related Article: Dongliang Shi, Xiaoping Yang, Yanan Ma, Mengyu Niu, Richard A. Jones|2020|Chem.Commun.|56|8651|doi:10.1039/D0CC04242K
DOI: 10.5517/ccdc.csd.cc254r69
title: CCDC 2000560: Experimental Crystal Structure Determination

description: Related Article: Natalie A. Pace, Brandon K. Rugg, Christopher H. Chang, Obadiah G. Reid, Karl J. Thorley, Sean Parkin, John E. Anthony, Justin C. Johnson|2020|Chemical Science|11|7226|doi:10.1039/D0SC02497J
DOI: 10.5517/ccdc.csd.cc254r7b
title: CCDC 2000561: Experimental Crystal Structure Determination

description: Related Article: Natalie A. Pace, Brandon K. Rugg, Christopher H. Chang, Obadiah G. Reid, Karl J. Thorley, Sean Parkin, John E. Anthony, Justin C. Johnson|2020|Chemical Science|11|7226|doi:10.1039/D0SC02497J
DOI: 10.5523/bris.2veuu177pu1ru2epio7kkldpcv
title: Datasets for Acousto-optic modulators built using PiezoMUMPS process

description: Contains all the raw data used to generate the figures in the paper: Valle and Balram, Optics Letters 44(15) 3777-3780 2019.
DOI: 10.5281/zenodo.3553833
title: Dataset for the article "Cost-effective Potential for Accurate Polarizable Embedding Calculations in Protein Environments"

description: This dataset contains additional material related to the article: "Cost-effective Potential for Accurate Polarizable Embedding Calculations in Protein Environments". Final version of the article is available at https://doi.org/10.1021/acs.jctc.9b00616 (paywall). A preprint is freely available at https://doi.org/10.26434/chemrxiv.8126912. See <em>readme.md</em> for further description.
DOI: 10.5281/zenodo.3250016
title: Dataset for the article "Cost-effective Potential for Accurate Polarizable Embedding Calculations in Protein Environments"

description: This dataset contains additional material related to the article: "Cost-effective Potential for Accurate Polarizable Embedding Calculations in Protein Environments". Final version of the article is available at https://doi.org/10.1021/acs.jctc.9b00616 (paywall). A preprint is freely available at https://doi.org/10.26434/chemrxiv.8126912. See <em>readme.md</em> for further description.
DOI: 10.25345/c5hb44
title: MassIVE MSV000085894 - UPLC-MS/MS data of R. mucilaginosa 50-3-19/20B
title: mgf files, quantification tables and metadata files
DOI: 10.5523/bris.dss2hubx4pk9zvg9x83k2mi2
title: PURE-LET denoising code

description: MATLAB code for PURE-LET denoising of spine calcium transients