InvestigationsWhat is an Investigation?
Here we collect curated data to be integrated into public repositories
Current chemical concept recognition tools have demonstrated significantly lower performance for in full-text articles than in abstracts. Improving automated full-text chemical concept recognition can substantially accelerate manual indexing and curation and advance downstream NLP tasks such as relevant article retrieval. Participating in BioCreative Track NLM-Chem we focus identifying chemicals in full-text articles (i.e. named entity recognition and normalization).
In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Improving the NER’s performance will directly have a positive impact on extracting relations between those entities. In recent years, deep learning has become the main research direction of NER due to the development of effective models. Language transformer models like e.g. BERT are frequently used because they enable the specialisation of models by domain-specific ...
TA3 focusses on the services, service enabling tools, and software that NFDI4Health will provide to the user community. Most services and tools will be based on open source software that has already been developed by the (co-)applicants or by the broader scientific developer community. In close cooperation with TA4 and TA5, use case requirements and community feedback will help to further develop these tools and to foster interoperability of currently fragmented IT solutions for storage of metadata, ...
NFDI4Health task area 2 targets core deficits in medical sciences, i.e. the lack of harmonised standards for data and data quality management in clinical trials, public health surveys, and epidemiological cohorts, as well as the lack of information on and access to relevant standards. By making standards available, TA2 will improve the findability, accessibility and interoperability of existing and novel data bodies. For this purpose, guidelines, standards and policies on data management and ...
Submitter: Martin Golebiewski
Studies: NFDI4Health T2.1: Data management and publication policies, NFDI4Health T2.2: Data and metadata standards and integration, NFDI4Health T2.3: Data quality and data provenance, NFDI4Health T2.4: Standardisation of health data access and interoperabi...
Assays: No Assays
With its focus on interaction, networking and exchange, task area 4 addresses the overall NFDI4Health Key Objective to support cooperation between clinical research, epidemiological and public health communities. It also provides training and education for the health research community and beyond, focusing on FAIR data principles.
Shared Space on OneDrive: https://onedrive.live.com/?id=4E57D2DCFD31954C%213715&cid=4E57D2DCFD31954C
The main objective of task area 5 of NFDI4Health is to implement or to at least explore the possibilities to implement these infrastructure components in specific use cases which reflect core needs of the scientific community related to health data research. The use cases will address a range of areas which will lay the ground for future expansion for full coverage of the broad range of data collected in health research.
Data protection regulations have to be taken into account on many levels of the infrastructure developed by NFDI4Health. Moreover, the health data managed by NFDI4Health belong to the so-called special categories of personal data, the processing of which is subject to particularly strict data protection requirements. But nevertheless, data protection law contains a variety of regulatory approaches of data processing for scientific research purposes, which are all aimed at a privileged treatment ...
Submitter: Theresa Kouril
Assays: GAPDH and flux inhibition in MCF7 and MDA231cells, HK, GAPDH and flux inhibition in MDA231 cells, Incubation time for inhibitor treatment, Target verification/ Specificity of 3BrP in glycolysis, Traget verification/ Specificity of IAA in glycolysis
The investigation entails the construction and validation of a detailed mathematical model for glycolysis erythrocytes infected with the malaria parasite Plasmodium falciparum in the blood stage form.
Submitter: Dawie van Niekerk
Assays: Flux vs external glucose, Flux vs parasitaemia, GLC incubation, Inhibition of glycolytic flux, Malaria parasite volume determinations, Metabolic control analysis, Stage specific fluxes, Steady-state
Submitter: Charles Demurjian
Assays: All Metadata, All Metadata, Cell Culture Imaging - Data Linked, Cell Culture and Organoid Generation - Metadata, DNA Extraction - Metadata, DNA Extraction - Metadata, Elisa - Data Linked, Gene Expression Analysis - Data Linked, Immunohistochemistry - Data Linked, Linear Mixed Model - Data Linked, Luminex - Data Linked, Mass Spectrometry Proteomics - Data Linked, Mass Spectrometry Proteomics Analysis - Data Linked, Patient Visit - Metadata, Patient Visit - Metadata, Short Read Sequencing - Data Linked, Single Cell Expression Matrix Analysis - Data Linked, Single Cell Sequencing - Data Linked, Tissue Collection - Metadata, Tissue Collection - Metadata
Submitter: Christoff Odendaal
Assays: ACAD activity partitioning, Comparing acyl-CoA dehydrogenase deficiencies, HepG2 oxygen consumption, MCADD patient personalised modelling, MCADD rescue titration, Metabolic control analysis, Models, Predicting urinary acylcarnitines under metabolic decompensation., Whole-body ketogenic flux
User metadata is an essential part of experimental data. Scientists need to understand underlying conditions and experimental procedures in order to model or investigate relevant biological questions. Currently, only a small fraction of the High Content SCreening (HCS) investigations are deposited for reuse by the community, and an even smaller fraction of that data is standards-compliant. For reusing data, scientists need to be able to understand how data was generated, under which experimental ...
Submitter: Katy Wolstencroft
Studies: MIHCSME templates
Assays: General MIHCSME template, MIHCSME template example for "Integration of biological data by kernels ..., MIHCSME template example for "Uncovering the signaling landscape control..., MIHCSME template example for compound screen on HepG2 CHOP-GFP reporter ..., MIHCSME template example for “Temporal single cell cellular stress respo...
Submitter: Charles Demurjian
Assays: Cancer Cell Extravasation Analysis - Data Linked, Clot Modeling Analysis - Data Linked, Device Imaging - Data Linked, Flow Cytometry - Data Linked, Flow Cytometry Analysis - Data Linked, Microfluidic Device Creation - Metadata, Permeability Analysis - Data Linked
Design, synthesis, computational studies and biological evaluation of antiparasitic dinitroaniline-ether phospholipid hybrids
Submitter: Ina Poehner
Virtual Tissues (VTs) are multi-scale, multi-cellular, mechanistic Agent Based Models (ABMs) that predict the spatio-temporal dynamics of biological tissues. While multiple platforms exist for constructing and executing VT models (listed below in the section on VT Modeling Frameworks), models developed for different platforms are currently incompatible and not accessible or executable in a common location, impeding model discovery, validation and reuse. FAIRSPACE will initially provide support ...
Submitter: Rahuman Sheriff
Studies: No Studies
Assays: No Assays
This investigation serves as supplementary material for a SWAT4HCLS publication that describes minimum metadata and provenance requirements for reproducible enrichment analysis results.
Functional enrichment analysis is an essential downstream process in high throughput omics studies, such as transcriptomics and proteomics. By using the Gene Ontology (GO) and its annotations (GOA), underlying functional patterns of over-representation can be identified, leading to ...
Aims: The immune response is important for mediating the benefit of cardiac cell therapies. The role of varied immune responses in influencing the outcome of cardiomyocyte cell transplantation after myocardial infarction was investigated. Methods and Results: Cardiac flow cytometric analysis of C57BL/6J and T- and B cell deficient Rag2del mice revealed varied CD11b, natural killer and dendritic cell responses following sham injection and a disparate macrophage response after myocardial infarction. ...
Data, FMv2 model and simulations for the Chew et al. 2017 paper (bioRxiv https://doi.org/10.1101/105437 ), updated in 2022, mostly on the prr7 prr9 double mutant, with controls in lsf1 and prr7 single mutants. This is one of the outputs from the EU FP7 TiMet project, https://fairdomhub.org/projects/92.
This data archive was updated during submisson to the journal _in Silico _Plants in 2022, and a Snapshot was published. The updates are not changing the core data or the FMv2 model that has been ...
Submitter: Andrew Millar
Studies: Analysis of Framework Model version 2 (FMv2), Construction of Framework Model version 2 (FMv2), Test of FMv2, follow-on: mechanisms of malate/fumarate accumulation, Test of FMv2, photoperiodic flowering and hypocotyl elongation, Test of FMv2, study Gibberellins 1, Test of FMv2, study Laurel & Hardy 1, Test of FMv2, study Laurel & Hardy 2, Test of FMv2, study Laurel & Hardy 3, Tests of FMv2, compilations and figures
Assays: Assimilation and partitioning of 14CO2 at night, Biomass and metabolites, Biomass and metabolites, Biomass and metabolites, Biomass, leaf area and gas exchange data, Biomass, leaf number and metabolites, Circadian period analysis, Composition of FMv2, FMv2 simulation, FMv2 simulation, FMv2 simulation, Mizuno lab, Flowering time in clock mutants, Mizuno lab, Hypocotyl length in clock mutants, Relationship among FMv2 outputs, Sensitivity analysis of FMv2, Simulating clock gene expression with model P2011.1.2, Thiamine vitamers, TiMet WP1.1, Clock gene expression in clock mutants, TiMet WP1.1a Metabolite analysis of clock mutants
Short Name: T21_SXPsysbio Title: Use a systems biology approach to identify regulatory bottlenecks in SxPv1 Description: Samples from SXPv1.0 plants as well as sister nulls (progeny from the original transgenic event in which the transgene has segregated) and wild type will be grown and leaf samples taken for RNA extraction and profiling of primary metabolites and volatiles (target pheromones as well as potential derivatives) (P1, P5). Phenotypic and GC-MS data will be obtained and analysed from ...
Submitter: Marko Petek
Studies: Investigation files, _S_P1_SPv10T0andT1, _S_P1_SPv10T2andT3, _S_P1_SPv1TransientExp, _S_P1_SxPAltAcTransferases, _S_P1_SxPv10vsSxP12, _S_P1_SxPv12T2, _S_P4_CoExpNetViz, _S_P4_DiNAR, _S_P4_GAtreat, _S_P4_SxP10-newG-DE, _S_P4_SxP10-oldG-DE, _S_P4_SxP1012-finalG, _S_P4_SxP12-newG-DE
Assays: _A_00_SxP_photos-phenotyping, _A_01_RNA1-RNAisol, _A_01_SxP_Data_Only-CoExp, _A_01_SxPv12_fastq-QC, _A_01_mapping-CLC, _A_01_toNewGenome-CLC-mapping, _A_02_FastQC-bioinfo, _A_02_Nb_datasets-CoExp, _A_02_SxPv12_mapping-CLC, _A_02_limmavoomDE-R, _A_02a_limmavoom-multim-R, _A_02a_limmavoomDEbylines-R, _A_02b_limmavoom-uniquem-R, _A_03_MapMan-visualisation, _A_03_NewGenome-MapMan, _A_03_SxPv12_limmavoom_DE-R, _A_03_mapping-CLC, _A_03a_mapping2-STAR, _A_04_GSEA-Stat, _A_04_MapManBINenrich-GSEA, _A_04_Mercator-bioinfo, _A_04_SxPv12_GeneSetEnrichment-RNAseg-GSEA, _A_05_DEstat-R, _A_05_Phenotype_analysis-Stat, _A_05_VOCcomp-Bioinfo, _A_05a_DEstat2-R, _A_05b_DElow-wt-R, _A_06_MapMan-bioinfo, _A_06_SxPv1-0_Illumina-Centrifuge, _A_07_NbAUSv1-0-InterPro, _A_07_transgenes-CLC, _A_CKN-DiNAR, _A_CKN_NbL35-DiNAR, _A_LeavesSxPv10vsv12-GCMS, _A_P4_v10v12-phenotyping, _A_PIS-DiNAR, _A_PIS-SxPv12-DiNAR, _A_PIS_NbL35-DiNAR, _A_RootsSxPv10vsv12-GCMS, _A_SP10T0Analysis-GCMS, _A_SP10T1Analysis-GCMS, _A_SPv10EaDActAnalysis-GCMS, _A_SPv10T2Analysis-GCMS, _A_SPv10T3Analysis-GCMS, _A_SPv10_phenotyping-Images, _A_SxPAlternativeAcetyltransferases-GCMS, _A_SxPv10vsv12-phenotyping, _A_SxPv12ScreeningT2-GCMS, _A_TransientSPv11andSPv12-GCMS, _I_T21_SXPsysbio-files, _S_P1_SPv10T0andT1-files, _S_P1_SPv10T2andT3-files, _S_P1_SPv1TransientExp-files, _S_P1_SxPAltAcTransferases-files, _S_P1_SxPv10vsSxP12-files, _S_P1_SxPv12T2-files, _S_P4_CoExpNetViz-files, _S_P4_DiNAR-files, _S_P4_GAtreat-files, _S_P4_SxP10-newG-DE-files, _S_P4_SxP10-oldG-DE-files, _S_P4_SxP1012-finalG-files, _S_P4_SxP12-newG-DE-files
We performed topological analysis on pathways from a harmonised dataset containing pathways from the COVID-19 Disease Map, WikiPathways, and Reactome. The analysis was done using Vanted, SBGN-ED, and LMME which support the import and export of several standard formats (such as SBML, and SBGN-ML).
Submitter: Felicia Burtscher
Assays: No Assays
Members of the genus Aromatoleum are cosmopolitan in diverse habitats and utilize a broad range of recalcitrant organic molecules coupled to denitrification or O2-respiration. To gain a holistic understanding of the model organism A. aromaticum EbN1T, we here studied its catabolic network dynamics in response to 3-(4-hydroxyphenyl)propanoate, phenylalanine, 3-hydroxybenzoate, benzoate and acetate utilized under nitrate-reducing vs. oxic conditions. Multi-OMICS (transcriptome, proteome and metabolome) ...
Submitter: Meina Neumann-Schaal
Assays: CoA LC/MS Data, Cultivation for multi-OMICS, EbN1 Genome re-annotation, Metabolic modeling of EbN1, Proteomic data, Scenario files for Metano metabolic modeling, Transcriptomic data, non-volatile metabolites GC/MS
Data integration is an essential part of Systems Biology. Scientists need to combine different sources of information in order to model biological systems, and relate those models to available experimental data for validation. Currently, only a small fraction of the data and models produced during Systems Biology investigations are deposited for reuse by the community, and only a smaller fraction of that data is standards compliant, semantic content. By embedding semantic technologies into familiar ...
Submitter: Olga Krebs
Assays: Affy Transcriptomics Templates, Chip-chip Excel Template, General Transcriptomics Templates, Metabolomics Master Template, NimbleGen Transcriptomics Templates, Proteomics Template (gel electrophoresis), Proteomics Templates (Mass spectrometry), RT-PCR Excel Template, Standard-based Excel template for metabolomics data
Short Name: 03_Omics Title: Omics analysis of RNAi response in CPB Description: Transcriptomics and metagenome changes upon feeding CPB larvae with dsRNA Phenodata: ./phenodata_20210115.txt pISA Investigation creation date: 2021-01-15 pISA Investigation creator: Marko Petek Principal investigator: Marko Petek License: CC BY 4.0 Sharing permission: Private Upload to FAIRDOMHub: Yes
Submitter: Marko Petek
Assays: _A_01-DNAisol, _A_01_RNA-Seq_dsEGFP-NGS, _A_02-DNASeq, _A_02_CLC-RNASeq, _A_03-Centrifuge, _A_04_DE_divers-R, _A_05_extr_reads-rcf, _A_06_extr_bact-assembly, _A_07_allReads_meta-assembly, _I_03_Omics-files, _S_01_ns-dsRNA_trans-files, _S_02_metagenome_resp-files
Short Name: 02_FieldTrials Title: Field trials Description: Field trials - spraying CPB larvae on potato field with the insecticidal dsRNA validated for effectiveness in the laboratory trials Phenodata: ./phenodata_20210115.txt pISA Investigation creation date: 2021-01-15 pISA Investigation creator: Marko Petek Principal investigator: Marko Petek License: CC BY 4.0 Sharing permission: Private Upload to FAIRDOMHub: Yes