cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ghhxnkk8bt.1
title: Bitcoin-Color 4200: Candlestick Color Prediction Dataset Using Ichimoku Signals and Additional Features

description: The Bitcoin-Color 4200 dataset comprises 4,281 daily samples from the Bitcoin 24h chart between January, 2012 to July, 2023, created for a binary classification problem to predict the color of the next day’s candlestick—Red (price decrease) or Green (price increase). Each sample includes 28 input features, primarily derived from Ichimoku Clouds, such as the Conversion Line, Base Line, Leading Span A & B, and Lagging Span (Tenkan-sen, Kijun-sen, Senkou Span A, Senkou Span B, Chikou Span), alongside other suggested features. The output is a binary label indicating the predicted candlestick color, enabling machine learning models to forecast daily price movements. Candlestick color prediction is essential in trading and technical analysis, as it helps traders anticipate price direction, optimize strategies, and manage risk. The Ichimoku system, with its ability to capture trends, momentum, and key support/resistance zones, is central to this dataset, offering powerful insights into market behavior. By combining these signals with additional technical indicators, the dataset provides enhanced predictive capabilities, making it an excellent resource for research in financial forecasting, algorithmic trading, and cryptocurrency market analysis.
DOI: 10.17044/scilifelab.16451112
title: Autoantibody profiles associated with clinical features in psychotic disorders

description: Dataset from Jernbom Falk, A., Galletly, C., Just, D. et al. Autoantibody profiles associated with clinical features in psychotic disorders. Transl Psychiatry 11, 474 (2021).<br>Abstract:Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.<br><br>This dataset contains relative continuous as well as binarized autoantibody data, with associated cytokine and clinical data. These data constitute sensitive personal information that fall under the GDPR. Therefore, access to data and related code is restricted. The data can be made available for validation purposes, upon reasonable request and in accordance with GDPR.<br>A reasonable request should contain:1) Name of PI and host organisation2) Contact details3) Scientific purpose of data access request4) Commitment to inform when the data has been used in a publication5) Commitment not to host or share the data outside the requesting organisation6) Statement of non-commercial use of data
DOI: 10.17044/scilifelab.16451112.v1
title: Autoantibody profiles associated with clinical features in psychotic disorders

description: Dataset from Jernbom Falk, A., Galletly, C., Just, D. et al. Autoantibody profiles associated with clinical features in psychotic disorders. Transl Psychiatry 11, 474 (2021).<br>Abstract:Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.<br><br>This dataset contains relative continuous as well as binarized autoantibody data, with associated cytokine and clinical data. These data constitute sensitive personal information that fall under the GDPR. Therefore, access to data and related code is restricted. The data can be made available for validation purposes, upon reasonable request and in accordance with GDPR.<br>A reasonable request should contain:1) Name of PI and host organisation2) Contact details3) Scientific purpose of data access request4) Commitment to inform when the data has been used in a publication5) Commitment not to host or share the data outside the requesting organisation6) Statement of non-commercial use of data
DOI: 10.7483/opendata.cms.iaez.yk16
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.16677252.v1
title: BAGS.v1: BAltic Gene Set gene catalogue

description: The BAltic Gene Set (BAGS.v1) gene catalogue encompasses 67,566,251 genes. The 67 million genes are based on metagenomic data from Alneberg at al. (2020) from 124 seawater samples that span the salinity and oxygen gradients of the Baltic Sea and capture seasonal dynamics at two locations. To obtain the gene catalogue, we used a mix-assembly approach described in Delgado et al. (2021). The gene catalogue has been functionally and taxonomically annotated, as described in Delgado et al. (2021). Here you find gene and protein sequences, and different types of annotations for the proteins. Also, contigs for the co-assembly are included (see the Delgado paper).<br>When using the BAGS gene catalogue, please cite:<br><br><br>1. Delgado LF, Andersson AF (2021) Evaluating metagenomic assembly approaches for biome-specific gene catalogues. Submitted for publication.<br><br>2. Alneberg J, Bennke C, Beier S, Bunse C, Quince C, Ininbergs K, Riemann L, Ekman M, Jürgens K, Labrenz M, Pinhassi J, Andersson AF (2020) Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun Biol 3, 119 (2020)<br><br>
DOI: 10.7483/opendata.cms.fsbc.cx4x
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14653179
title: iRead4Skills - Data set 2: annotated corpora report

description: Detailed overview of Dataset 2: annotated corpora by level of complexity for FR, PT and SP. It is organized into three major sections -- 2. Classification and annotation tasks, 3. Data description, and 4. Sample results -- and provides a detailed account on the classification and annotation processes undertaken, the composition of the dataset, including data format, qualitative and quantitative analyses and inter-annotators agreement, as well as a sample of annotated texts extracted from the dataset.
DOI: 10.5281/zenodo.14653180
title: iRead4Skills - Data set 2: annotated corpora report

description: Detailed overview of Dataset 2: annotated corpora by level of complexity for FR, PT and SP. It is organized into three major sections -- 2. Classification and annotation tasks, 3. Data description, and 4. Sample results -- and provides a detailed account on the classification and annotation processes undertaken, the composition of the dataset, including data format, qualitative and quantitative analyses and inter-annotators agreement, as well as a sample of annotated texts extracted from the dataset.
DOI: 10.7483/opendata.cms.0uv9.98tx
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.16866202
title: Data and most relevant results for the FoldDock project

description: Data and main results for the study <b>"Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments."</b><b></b><br>Contained datasets consist of:<b>1 -</b> 219 heterodimers from dockground benchmark 4 dataset <b>2 -</b> 1503 heterodimeric structures from a recent study (Green, A. G. et al. Nat. Commun. 12, 1–12 (2021))<b>3 -</b> 7 heterodimeric complexes from CASP14<b>4 -</b> 8 novel heterodimeric complexes deposited in the PDB database after 15 June 2021<br>For each one of the mentioned datasets it can be found in this repository:- concatenated protein sequences in FASTA format;- 3D structure atomic coordinates in PDB format;<br>- csv files summarizing chain IDs, chain lengths and other dataset parameters;- msas for pair of sequences, required to run AlphaFold2<br><br>For dataset 1 and 2, AlphaFold2 output docking results for 5 identical runs are provided as well, together with a csv summarizing dockq scores and different model quality metrics.<br>
DOI: 10.17044/scilifelab.16866202.v2
title: Data and most relevant results for the FoldDock project

description: Data and main results for the study <b>"Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments."</b><b></b><br>Contained datasets consist of:<b>1 -</b> 219 heterodimers from dockground benchmark 4 dataset <b>2 -</b> 1503 heterodimeric structures from a recent study (Green, A. G. et al. Nat. Commun. 12, 1–12 (2021))<b>3 -</b> 7 heterodimeric complexes from CASP14<b>4 -</b> 8 novel heterodimeric complexes deposited in the PDB database after 15 June 2021<br>For each one of the mentioned datasets it can be found in this repository:- concatenated protein sequences in FASTA format;- 3D structure atomic coordinates in PDB format;<br>- csv files summarizing chain IDs, chain lengths and other dataset parameters;- msas for pair of sequences, required to run AlphaFold2<br><br>For dataset 1 and 2, AlphaFold2 output docking results for 5 identical runs are provided as well, together with a csv summarizing dockq scores and different model quality metrics.<br>
DOI: 10.17044/scilifelab.16866202.v1
title: Data and most relevant results for the FoldDock project

description: Data and main results for the study <b>"Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments."</b><b></b><br>Contained datasets consist of:<b>1 -</b> 219 heterodimers from dockground benchmark 4 dataset <b>2 -</b> 1503 heterodimeric structures from a recent study (Green, A. G. et al. Nat. Commun. 12, 1–12 (2021))<b>3 -</b> 7 heterodimeric complexes from CASP14<b>4 -</b> 8 novel heterodimeric complexes deposited in the PDB database after 15 June 2021<br>For each one of the mentioned datasets it can be found in this repository:- concatenated protein sequences in FASTA format;- 3D structure atomic coordinates in PDB format;<br>- csv files summarizing chain IDs, chain lengths and other dataset parameters;<br>For dataset 1 and 2, AlphaFold2 output docking results for 5 identical runs are provided as well, together with a csv summarizing dockq scores and different model quality metrics.<br>
DOI: 10.7483/opendata.cms.uu15.t3ol
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.16945039
title: Protein models for "Towards a structurally resolved human protein interaction network"

description: Dataset descriptionThis is all prediction associated with the paper entitled 'Towards a structurally resolved human protein interaction network'.<br>Predicted protein models for 65000 human protein-protein interactions. Five datasets pairs: HuRUm HuMap2, Random single chain: HuRI and HuMap.<br>Additional resources can be found here: https://archive.bioinfo.se/huintaf2/Protein interaction data and annotationsHuman protein pairs known to physically interact were obtained from the Hu.MAP dataset, retaining pairwise interactions with &gt;=0.5 confidence, and most interactions from the HuRI dataset.Protein complex predictionTo predict protein complexes of pairwise interactions, we used the FoldDock pipeline (Bryant et al., 2021) based on AlphaFold2 (Jumper et al., 2021). We use the option of fused+paired multiple sequence alignments (MSAs) and run the model configuration m1-10-1 as this provides the highest success rate accompanied by a 20-fold speed-up. Both the fused and paired MSAs are constructed from running HHblits on every single chain against Uniclust30. The fused MSA is generated by simply concatenating the output of each of the single-chain HHblits runs for two interacting chains. The paired MSA is constructed by combining the top hit for each matching OX identifier between two interacting chains, using the output from the single-chain HHblits runs. <br>
DOI: 10.17044/scilifelab.16945039.v1
title: Protein models for "Towards a structurally resolved human protein interaction network"

description: Dataset descriptionThis is all prediction associated with the paper entitled 'Towards a structurally resolved human protein interaction network'.<br>Predicted protein models for 65000 human protein-protein interactions. Five datasets pairs: HuRUm HuMap2, Random single chain: HuRI and HuMap.<br>Additional resources can be found here: https://archive.bioinfo.se/huintaf2/Protein interaction data and annotationsHuman protein pairs known to physically interact were obtained from the Hu.MAP dataset, retaining pairwise interactions with &gt;=0.5 confidence, and most interactions from the HuRI dataset.Protein complex predictionTo predict protein complexes of pairwise interactions, we used the FoldDock pipeline (Bryant et al., 2021) based on AlphaFold2 (Jumper et al., 2021). We use the option of fused+paired multiple sequence alignments (MSAs) and run the model configuration m1-10-1 as this provides the highest success rate accompanied by a 20-fold speed-up. Both the fused and paired MSAs are constructed from running HHblits on every single chain against Uniclust30. The fused MSA is generated by simply concatenating the output of each of the single-chain HHblits runs for two interacting chains. The paired MSA is constructed by combining the top hit for each matching OX identifier between two interacting chains, using the output from the single-chain HHblits runs. <br>
DOI: 10.7483/opendata.cms.6caz.7lcb
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.17012382
title: Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions

description: Dataset used in the paper "Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions".<br>The GTPase4.rar file contains the raw confocal images of HT1080 cell line stably expressing RhoA-2G or Rac-2G biosensors seeded on 6.9kPa polyacrylamid gel. Channel 1 and 2 are the FRET signals (excite 457nm, emission 482/35 nm and 525/50 nm). Channel 3 is the red fluorescent beads for the traction force calculation (by comparing to the corresponding cell free image file (containing "RL" in the file name)). Channel 4 is the transmission light for the cell morphology. <br>The Optogenetic.rar file contains the raw confocal images of HT1080 cell line stably expressing PA-Rac1(Q61L) or PA-Rac1(T17N) constructs perturbed by 488 nm light before and after 5 min imaging on the 6.9kPa polyacrylamide gel. Channel 1 is the PA-Rac1 constructs signal. Channel 2 is the far red fluorescent beads for the traction force calculation(by comparing to the corresponding cell free image file (containing "RL" in the file name)). The 3D collagen folder contains the representative raw confocal images of same stable HT1080 cell line seeded in the 3D collagen perturbed by 488 nm light before and after 5 min imaging. <br>The LLSM.rar file contains the representative lattice light sheet images of the HT1080 cell line stably expressing RhoA-2G or Rac1-2G biosensors seeded on the 6.9kPa polyacrylamide gel. The file is in .ims format which could be opened with Imaris software. <br>The WindowingResults.rar file contains the window sampled results from the GTPase4.rar and Optogenetic.rar.<br>The Codes.rar file contains the software and custom codes used in this study.
DOI: 10.17044/scilifelab.17012382.v2
title: Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions

description: Dataset used in the paper "Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions".<br>The GTPase4.rar file contains the raw confocal images of HT1080 cell line stably expressing RhoA-2G or Rac-2G biosensors seeded on 6.9kPa polyacrylamid gel. Channel 1 and 2 are the FRET signals (excite 457nm, emission 482/35 nm and 525/50 nm). Channel 3 is the red fluorescent beads for the traction force calculation (by comparing to the corresponding cell free image file (containing "RL" in the file name)). Channel 4 is the transmission light for the cell morphology. <br>The Optogenetic.rar file contains the raw confocal images of HT1080 cell line stably expressing PA-Rac1(Q61L) or PA-Rac1(T17N) constructs perturbed by 488 nm light before and after 5 min imaging on the 6.9kPa polyacrylamide gel. Channel 1 is the PA-Rac1 constructs signal. Channel 2 is the far red fluorescent beads for the traction force calculation(by comparing to the corresponding cell free image file (containing "RL" in the file name)). The 3D collagen folder contains the representative raw confocal images of same stable HT1080 cell line seeded in the 3D collagen perturbed by 488 nm light before and after 5 min imaging. <br>The LLSM.rar file contains the representative lattice light sheet images of the HT1080 cell line stably expressing RhoA-2G or Rac1-2G biosensors seeded on the 6.9kPa polyacrylamide gel. The file is in .ims format which could be opened with Imaris software. <br>The WindowingResults.rar file contains the window sampled results from the GTPase4.rar and Optogenetic.rar.<br>The Codes.rar file contains the software and custom codes used in this study.
DOI: 10.7483/opendata.cms.1gid.iy8m
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.17012382.v1
title: Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions

description: Dataset used in the paper "Local temporal Rac1-GTP nadirs and peaks restrict cell protrusions and retractions".<br>The GTPase4.rar file contains the raw confocal images of HT1080 cell line stably expressing RhoA-2G or Rac-2G biosensors seeded on 6.9kPa polyacrylamid gel. Channel 1 and 2 are the FRET signals (excite 457nm, emission 482/35 nm and 525/50 nm). Channel 3 is the red fluorescent beads for the traction force calculation (by comparing to the corresponding cell free image file (containing "RL" in the file name)). Channel 4 is the transmission light for the cell morphology. <br>The Optogenetic.rar file contains the raw confocal images of HT1080 cell line stably expressing PA-Rac1(Q61L) or PA-Rac1(T17N) constructs perturbed by 488 nm light before and after 5 min imaging on the 6.9kPa polyacrylamide gel. Channel 1 is the PA-Rac1 constructs signal. Channel 2 is the far red fluorescent beads for the traction force calculation(by comparing to the corresponding cell free image file (containing "RL" in the file name)). The 3D collagen folder contains the representative raw confocal images of same stable HT1080 cell line seeded in the 3D collagen perturbed by 488 nm light before and after 5 min imaging. <br>The LLSM.rar file contains the representative lattice light sheet images of the HT1080 cell line stably expressing RhoA-2G or Rac1-2G biosensors seeded on the 6.9kPa polyacrylamide gel. The file is in .ims format which could be opened with Imaris software. <br>The related scripts for analyzing the data can be found from GitHub: https://github.com/hujianjiang/Rac1.
DOI: 10.17044/scilifelab.17049059
title: Linked-read whole-genome sequencing resolves common and private structural variants in multiple myeloma

description: This repository contains 10x Chromium linked-read WGS (lrWGS), RNAseq and H3K27Ac ChIPseq from multiple myeloma.
<br>
The data consists of fastq files from lrWGS of 37 individuals with data from tumor and matched normal tissue from 32 of them. Additionally, it contains fastq files from RNAseq of 32 of the 37 patients and H3K27Ac ChIPseq data from select patients.
<br>
The data set contains sensitive human genomic data and is under restricted access. Request for access can be made to datacentre@scilifelab.se.
DOI: 10.17044/scilifelab.17049059.v1
title: Linked-read whole-genome sequencing resolves common and private structural variants in multiple myeloma

description: This repository contains 10x Chromium linked-read WGS (lrWGS), RNAseq and H3K27Ac ChIPseq from multiple myeloma.
<br>
The data consists of fastq files from lrWGS of 37 individuals with data from tumor and matched normal tissue from 32 of them. Additionally, it contains fastq files from RNAseq of 32 of the 37 patients and H3K27Ac ChIPseq data from select patients.
<br>
The data set contains sensitive human genomic data and is under restricted access. Request for access can be made to datacentre@scilifelab.se.