Biodiversity datasets published in the e!DAL-PGP repository in 2023 (NFDI4Biodiversity)
Ontology highlight
ABSTRACT:
ORGANISM(S): Triticum aestivum (wheat) Hordeum vulgare (barley)
SUBMITTER:
PROVIDER: S-BSST1455 | biostudies-other |
SECONDARY ACCESSION(S): 10.5447/ipk/2023/0
REPOSITORIES: biostudies-other
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Project description: Not available
| S-BSST1453 | biostudies-other
Project description:Understanding of TMZ therapy during and after treatment in a mouse model
2023-08-10 | GSE227348 | GEO
Project description:Datasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. Even when data are collected and are publicly available, it is often difficult to locate them. We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search.
| S-EPMC8375514 | biostudies-literature
Project description:Background:A considerable portion of primary biodiversity data is digitally locked inside published literature which is often stored as pdf files. Large-scale approaches to biodiversity science could benefit from retrieving this information and making it digitally accessible and machine-readable. Nonetheless, the amount and diversity of digitally published literature pose many challenges for knowledge discovery and retrieval. Text mining has been extensively used for data discovery tasks in large quantities of documents. However, text mining approaches for knowledge discovery and retrieval have been limited in biodiversity science compared to other disciplines. New information:Here, we present a novel, open source text mining tool, the Biodiversity Observations Miner (BOM). This web application, written in R, allows the semi-automated discovery of punctual biodiversity observations (e.g. biotic interactions, functional or behavioural traits and natural history descriptions) associated with the scientific names present inside a corpus of scientific literature. Furthermore, BOM enable users the rapid screening of large quantities of literature based on word co-occurrences that match custom biodiversity dictionaries. This tool aims to increase the digital mobilisation of primary biodiversity data and is freely accessible via GitHub or through a web server.
| S-EPMC6344444 | biostudies-literature
Project description:BackgroundGenome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.ResultsTo practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.ConclusionsAs a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
| S-EPMC9985298 | biostudies-literature
Project description:Ahmed Scientific Advances 2023 data repository
| PRJNA944688 | ENA
Project description:The human microbiome project (HMP) has made it possible to test important ecological theories for arguably the most important ecosystem to human health-the human microbiome. Existing limited number of studies have reported conflicting evidence in the case of the neutral theory; the present study aims to comprehensively test the neutral theory with extensive HMP datasets covering all five major body sites inhabited by the human microbiome. Utilizing 7437 datasets of bacterial community samples, we discovered that only 49 communities (less than 1%) satisfied the neutral theory, and concluded that human microbial communities are not neutral in general. The 49 positive cases, although only a tiny minority, do demonstrate the existence of neutral processes. We realize that the traditional doctrine of microbial biogeography "Everything is everywhere, but the environment selects" first proposed by Baas-Becking resolves the apparent contradiction. The first part of Baas-Becking doctrine states that microbes are not dispersal-limited and therefore are neutral prone, and the second part reiterates that the freely dispersed microbes must endure selection by the environment. Therefore, in most cases, it is the host environment that ultimately shapes the community assembly and tip the human microbiome to niche regime.
| S-EPMC4985628 | biostudies-literature
Project description:With next-generation sequencing, the genomic data available for the characterization of integration sites (IS) has dramatically increased. At present, in a single experiment, several thousand viral integration genome targets can be investigated to define genomic hot spots. In a previous article, we renovated a formal CIS analysis based on a rigid fixed window demarcation into a more stretchy definition grounded on graphs. Here, we present a selection of supporting data related to the graph-based framework (GBF) from our previous article, in which a collection of common integration sites (CIS) was identified on six published datasets. In this work, we will focus on two datasets, ISRTCGD and ISHIV, which have been previously discussed. Moreover, we show in more detail the workflow design that originates the datasets.
| S-EPMC4874583 | biostudies-literature
Project description:BackgroundMultiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level.MethodsWe performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively.ResultsFirstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs.DiscussionsTogether, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma.
| S-EPMC5289111 | biostudies-literature
Project description:ObjectivesOpen-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies.MethodsWe systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse.ResultsWe discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset.ConclusionRadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics.Critical relevance statementThis study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models.Key points- Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.
| S-EPMC10716101 | biostudies-literature