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.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “DTI”
DTI data from 'Fiber architecture in the ventromedial striatum and its relation with the bed nucleus of the stria terminalis'
Open the record for dataset details and reuse information.
Dataset: Drilling Tools International Corporation (DTI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Myotonic dystrophy research: imaging result files ( DTI, VBM) from a 5-year longitudinal follow-up study
<p>This ZIP-file contains supplementary data belonging to the manuscript</p> <p><strong>"Tracking the brain in myotonic dystrophy: a 5-year longitudinal follow-up study", published in PLOS ONE.</strong></p> <p>In this manuscript we aimed to examine the natural history of brain involvement in adult-onset myotonic dystrophies type 1 and 2 (DM1, DM2). We conducted a longitudinal observational study to examine functional and structural cerebral changes in myotonic dystrophies. We enrolled 16 adult-onset DM1 patients, 16 DM2 patients, and 17 controls. At baseline (T1) and at follow-up (T2) participants underwent neurological, neuropsychological, and 3T-brain MRI examinations using identical study protocols that included voxel-based morphometry and diffusion tensor imaging.</p> <p>The ZIP-file contains imaging result files from the different statistical analyses. </p>
DTI predicts Mandarin Learning
<p>The nifti files, bvals, bvecs of each deidentified participants are included.</p> <p>A separate csv file describes basic demographic information and these participants' learning scores reported in the paper: </p> <p>Qi Z., Han M., Garel K., Chen E. S., & Gabrieli J. D. E. (2014). White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. <em>Journal of Neurolinguistics</em>, 33: 14-28.</p>
NNk_DTI
<p>These are the code and datasets to reproduce all findings from the following paper:</p> <p>Playe, B., Stoven, V. Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity. <em>J Cheminform</em> <strong>12, </strong>11 (2020). https://doi.org/10.1186/s13321-020-0413-0</p>
Data from: A prospective harmonized multicentre DTI study of cerebral white matter degeneration in ALS
<p>Objective: To evaluate progressive white matter (WM) degeneration in ALS.</p> <p>Methods: Sixty-six patients with ALS and 43 healthy controls were enrolled in a prospective, longitudinal, multicentre study in the Canadian ALS Neuroimaging Consortium (CALSNIC). Participants underwent a harmonized neuroimaging protocol across 4 centres including diffusion tensor imaging (DTI) for assessment of WM integrity. Three visits were accompanied by clinical assessments of disability (ALSFRS-R) and upper motor neuron (UMN) function. Voxel-wise whole brain and quantitative tractwise DTI assessments were done at baseline and longitudinally. Correction for site variance incorporated data from healthy controls and from healthy volunteers that underwent the DTI protocol at each centre.</p> <p>Results: ALS patients had a mean progressive decline in fractional anisotropy (FA) of the corticospinal tract (CST) and frontal lobes. Tractwise analysis revealed reduced FA in the CST, corticopontine/corticorubral and corticostriatal tracts. CST FA correlated with UMN function and frontal lobe FA with the ALSFRS-R. A progressive decline in CST FA correlated with a decline in the ALSFRS-R and worsening UMN signs. Patients with fast vs slow progression had a greater reduction in FA of the CST and upper frontal lobe.</p> <p>Conclusions: Progressive WM degeneration in ALS is most prominent in the CST and frontal lobes, and to a lesser degree in the corticopontine/corticorubral tracts and the corticostriatal pathways. With the use of a harmonized imaging protocol and incorporation of analytical methods to address site-related variances, this study is an important milestone towards developing DTI biomarkers for cerebral degeneration in ALS.</p>
Data from: Non-invasive imaging of CSF-mediated brain clearance pathways via assessment of perivascular fluid movement with DTI MRI
The glymphatics system describes a CSF-mediated clearance pathway for the removal of potentially harmful molecules, such as amyloid beta, from the brain. As such, its components may represent new therapeutic targets to alleviate aberrant protein accumulation that defines the most prevalent neurodegenerative conditions. Currently, however, the absence of any non-invasive measurement technique prohibits detailed understanding of glymphatic function in the human brain and in turn, it's role in pathology. Here, we present the first non-invasive technique for the assessment of glymphatic inflow by using an ultra-long echo time, low b-value, multi-direction diffusion weighted MRI sequence to assess perivascular fluid movement (which represents a critical component of the glymphatic pathway) in the rat brain. This novel, quantitative and non-invasive approach may represent a valuable biomarker of CSF-mediated brain clearance, working towards the clinical need for reliable and early diagnostic indicators of neurodegenerative conditions such as Alzheimer's disease.
Datasets for ALADIN DTI prediction method
<p>Datasets used for the evaluation of the ALADIN method.</p> <p>This is a copy of following datasets:</p> <p>http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/</p> <p>http://staff.cs.utu.fi/~aatapa/data/DrugTarget/</p>
MMF-DTI; precomputed protein features
<p>MMF-DTI</p> <p> </p> <p>Precomputed protein features via ProtBERT encoder.</p>
A unified DTI prediction framework based on knowledge graph and recommendation system
<p>## A unified DTI prediction framework based on knowledge graph and recommendation system</p> <p> </p> <p># Code and data description</p> <p>## Scripts</p> <p>- `kge_nfm.py`: the complement of the KGE_NFM & NFM methods.</p> <p>- `kge_rf.py`: the complement of the KGE_RF & RF methods.</p> <p>- `deepdit.py`: the complement of the MPNN_CNN & DeepDTI methods.</p> <p>- the complement of DTINet and DTiGEMS is tested based on their source packages (more in Prerequisites)</p> <p><br> </p> <p>## `data/` directory</p> <p>#### `yamanishi_08/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg_data/`: supporting knowledge graph data</p> <p>- `dt_all_08.csv`: whole DTI dataset</p> <p>- `791drug_struc.csv`: drugbank id and smiles of drugs</p> <p>- `989proseq.csv`: kegg id and sequences of proteins</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `BioKG/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `comp_struc.csv`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `hetionet/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `map_drugs_df`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `luo's_dataset/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `mapping/`: related mappings and similarity matrix (https://github.com/luoyunan/DTINet)</p> <p>- `protein.txt`: list of protein names</p> <p>- `disease.txt`: list of disease names</p> <p>- `se.txt`: list of side effect names</p> <p>- `drug_dict_map`: a complete ID mapping between drug names and DrugBank ID</p> <p>- `protein_dict_map`: a complete ID mapping between protein names and UniProt ID</p> <p>- `Similarity_Matrix_Drugs.txt` : Drug similarity scores based on chemical structures of drugs</p> <p>- `Similarity_Matrix_Proteins.txt` : Protein similarity scores based on primary sequences of proteins</p> <p>- `feature/`: related features used in methods</p> <p>- `drug_smiles.csv`: drugbank id and smiles</p> <p>- `seq.txt`: list of protein sequences</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `eg_model/` directory</p> <p>We provided a pre-trained kge model for example.</p> <p>- `dismult_400_warm_1_10.pkl`</p> <p><br> </p> <p># Prerequisites</p> <p>#### Operating system: Linux</p> <p>#### Programing language: python</p> <p>#### KGE_NFM & NFM dependencies</p> <p>```</p> <p>- python 3.6</p> <p>- pandas '1.1.5'</p> <p>- numpy '1.18.4'</p> <p>- scikit-learn '0.24.1'</p> <p>- tensorflow '1.15.0'</p> <p>- ampligraph '1.3.2'</p> <p>- deepctr '0.8.4'</p> <p>```</p> <p>#### baseline dependencies</p> <p>- RF & KGE_RF (included in KGE_NFM&NFM dependencies)</p> <p>- MPNN_CNN & DeepDTI:</p> <p>- source: https://github.com/kexinhuang12345/DeepPurpose</p> <p>```</p> <p>- deeppurpose '0.0.9'</p> <p>- torch '1.6.0+cu101'</p> <p>```</p> <p>- DTINet:</p> <p>- source: https://github.com/luoyunan/DTINet</p> <p>- note: in this work, we run the DTINet in a python environment, which need Linux system and python2. Importantly, this method requires the [Inductive Matrix Completion](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) (IMC) library. More detailed information about the installation of this method could be found in the source code of the DTINet.</p> <p>- DTiGEMS:</p> <p>- source: https://github.com/MahaThafar/DTiGEMSplus</p> <p>- TriModel:</p> <p>- source: http://drugtargets.insight-centre.org/</p> <p><br> <br> </p> <p># Example (kge_nfm.py)</p> <p> </p> <p>#### A brief presentation of the results:</p> <p>- return average loss when training kge model</p> <p>```</p> <p>Average Loss: 0.475181: 2%|###3 | 1/50 [01:10<57:31, 70.44s/epoch]</p> <p>```</p> <p>- return performance(mrr) on training set of DTI for early stopping (kge_model in `eg_model/`)</p> <p>```</p> <p>In [35]: roc = roc_auc(test_label,test_score)</p> <p>...: pr = pr_auc(test_label,test_score)</p> <p>...: print(roc)</p> <p>...: print(pr)</p> <p>0.8731770833333332</p> <p>0.44079654835037246</p> <p>```</p> <p> </p> <p>- nfm training process (`patience=10`)</p> <p> </p> <p>```</p> <p>In [45]: roc_nfm,pr_nfm,pred_y = train_nfm(feature_columns,train_model_input,train_label,test_model_input,test_label,patience)</p> <p>Train on 44851 samples</p> <p>Epoch 1/2000</p> <p>44851/44851 - 2s - loss: 0.5332 - precision: 0.0976</p> <p>Epoch 2/2000</p> <p>44851/44851 - 1s - loss: 0.4143 - precision: 0.0000e+00</p> <p>Epoch 3/2000</p> <p>44851/44851 - 1s - loss: 0.3456 - precision: 0.0000e+00</p> <p>Epoch 4/2000</p> <p>44851/44851 - 1s - loss: 0.3443 - precision: 0.0000e+00</p> <p>Epoch 5/2000</p> <p>44851/44851 - 1s - loss: 0.3470 - precision: 0.0000e+00</p> <p>Epoch 6/2000</p> <p>44851/44851 - 1s - loss: 0.3382 - precision: 0.0000e+00</p> <p>......</p> <p>Epoch 279/2000</p> <p>44851/44851 - 1s - loss: 0.0758 - precision: 0.9248</p> <p>Epoch 280/2000</p> <p>44851/44851 - 1s - loss: 0.0753 - precision: 0.9327</p> <p>Epoch 281/2000</p> <p>44851/44851 - 1s - loss: 0.0796 - precision: 0.9155</p> <p>Epoch 282/2000</p> <p>44851/44851 - 1s - loss: 0.0764 - precision: 0.9276</p> <p>Epoch 283/2000</p> <p>44851/44851 - 1s - loss: 0.0739 - precision: 0.9127</p> <p>```</p> <p> </p> <p>- reutrn results as type of roc_auc & pr_auc</p> <p>```</p> <p>0.9812476679104477</p> <p>0.8803416284646345</p> <p>```</p>
The Use of Resting State, fMRI and DTI in the Identification of Chronic Pain Conditions
ClinicalTrials.gov study NCT02987933. IPD Sharing: NO. Countries: 1. Publications: 5.
DTI of the Brain and Cervical Spine: Evaluation in Normal Subjects and Patients With Cervical Spondylotic Myelopathy
ClinicalTrials.gov study NCT01868958. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis
ClinicalTrials.gov study NCT05020626. IPD Sharing: Not stated. Countries: 1. Publications: 33.
MEG and DTI of Neural Function and Connectivity in Traumatic Brain Injury
ClinicalTrials.gov study NCT01298557. IPD Sharing: Not stated. Countries: 1. Publications: 5.
The Natural History of Traumatic Spinal Cord Injury Using fMRI, MRS and DTI
ClinicalTrials.gov study NCT00790361. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Longitudinal Assessment of Spinal Cord Structural Plasticity Using DTI in SCI Patients
ClinicalTrials.gov study NCT03069222. IPD Sharing: NO. Countries: 1. Publications: 16.
Diffusion Tensor Imaging (DTI) in Infants With Krabbe Disease
ClinicalTrials.gov study NCT00787865. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluating Structural and Functional Changes of Brain by fMRI and DTI in Patients With Intracranial Germ Cell Tumors
ClinicalTrials.gov study NCT03771625. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Investigation of Brain Nitrogen in Partial Ornithine Transcarbamylase Deficiency (OTCD) Using 1 H MRS, DTI, and fMRI
ClinicalTrials.gov study NCT01569568. IPD Sharing: YES. Countries: 0. Publications: 7.
Comparison of Intramuscular Distribution of Different Injection Volumes Via Diffusion Tensor Imaging (DTI)
ClinicalTrials.gov study NCT01162291. IPD Sharing: Not stated. Countries: 1. Publications: 5.
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.