Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “New Approach Methodologies”
Transcriptomic characterization of 2D and 3D human induced pluripotent stem cell-based in vitro models as New Approach Methodologies for developmental neurotoxicity testing
<p><strong>Abstract:</strong> The safety and developmental neurotoxicity (DNT) potential of chemicals remain critically understudied due to limitations of current in vivo testing guidelines, which are low throughput, resource-intensive, and hindered by species differences that limit their relevance to human health. To address these issues, robust new approach methodologies (NAMs) using deeply characterized cell models are essential. This study presents the comprehensive transcriptomic characterization of two advanced human-induced pluripotent stem cell (hiPSC)-derived models: a 2D adherent and a 3D neurosphere model of human neural progenitor cells (hiNPCs) differentiated up to 21 days. Using high-throughput RNA sequencing, we compared gene expression profiles of 2D and 3D models at three developmental stages (3, 14, and 21 days of differentiation). Both models exhibit maturation towards post-mitotic neurons, with the 3D model maturing faster and showing a higher prevalence of GABAergic neurons, while the 2D model is enriched with glutamatergic neurons. Both models demonstrate broad applicability domains, including excitatory and inhibitory neurons, astrocytes, and key endocrine and especially the understudied cholinergic receptors. Comparison with human fetal brain samples confirms their physiological relevance. This study provides novel in-depth applicability insights into the temporal and dimensional aspects of hiPSC-derived neural models for DNT testing. The complementary use of these two models is highlighted: the 2D model excels in synaptogenesis assessment, while the 3D model is particularly suited for neural network formation as observed as well in previous functional studies with these models. This research marks a significant advancement in developing human-relevant, high-throughput DNT assays for regulatory purposes.</p> <p><strong>This data sets contains:</strong></p> <p><strong>Tab. S1</strong> - Significant genes results</p> <p><strong>Tab. S2</strong> - Enriched pathways_GO_Biological Processes</p> <p><strong>Tab. S3</strong> - Enriched pathways_GO_Cellular Components</p> <p><strong>Tab. S4</strong> - Enriched pathways_GO_Molecular Function</p> <p><strong>Tab. S5</strong> - Enriched pathways_KEGG</p> <p><strong>Tab. S6</strong> - EnrichEnriched pathways_Panther</p> <p><strong>Tab. S7</strong> - Enriched pathways_Reactome</p> <p><strong>Tab. S8</strong> - Gene counts</p> <p><strong>Tab. S9</strong> - Gene selection for targeted analysis</p>
PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach
<p>This data set for the publication "The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach" in PlosONE (2021) comprises:</p> <p>EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.</p> <p>fNIRS: oxy-Hb and deoxy-Hb data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs</p> <p>Behavioral data: Task Performance during the recognition experiment (reaction times for and percentage of correctly identified learned and new PPPs)</p> <p> </p>
Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)
<p><span>The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Moreover, wet lab experimentation is associated with particularly high costs, does not adhere to the principle of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalyzed reaction to determine Michaelis–Menten maximal velocities (Vmax). The molecular fingerprints employed include RCDK standard fingerprints (1024 bits), MACCS keys (166 bits), PubChem fingerprints (881 bits), and E-States fingerprints (79 bits). These were integrated to produce reaction fingerprints. The data were sourced from SABIO RK, providing a concrete framework to support training procedures. After the data preprocessing stage, the dataset was randomly split into a training set (70%), a validation set (10%), and a test set (20%), ensuring unique amino acid sequences for each subset. The data points with structures similar to those used to train the model as well as uncommon reactions were employed to further test the model. The developed models were optimized during the training procedure to predict Vmax values efficiently and reliably. By utilizing a fully connected neural network, these models can be applied to all organisms. The amino acid proportions of enzymes were also tested, which revealed that the amino acid content was an unreliable predictor of the Vmax. During testing, the model demonstrated better performance on known structures than on unseen data. In the given use case, the model trained solely on enzyme representations achieved an R-squared of 0.45 on unseen data and 0.70 on known structures. When enzyme representations were integrated with RCDK fingerprints, the model achieved an R-squared of 0.46 for unseen data and 0.62 for known structures.</span></p>
Data and Python codes used in "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"
<p>These are the dataset and python codes used in a manuscript titled "A New Approach to Estimate Total Nitrogen Concentration in a Seasonal Lake Based on Multi-Source Data Methodology"</p>
Data from: Assessing environmental pollution in birds: a new methodological approach for interpreting bioaccumulation of trace elements in feather shafts using geochemical sediment data
Open the record for dataset details and reuse information.
Application of a New Approach Methodology (NAM)-based Strategy for Genotoxicity Assessment of Data-poor Compounds
GEO Series GSE213454. Homo sapiens. 276 samples. Type: Expression profiling by high throughput sequencing.
A New Approach Methodology (NAM) Based Assessment of Butylated hydroxytoluene (BHT) for Endocrine Disruption (ED) Potential
GEO Series GSE211553. Homo sapiens. 0 samples. Type: Expression profiling by array.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.