Find research datasets worth reusing

opencc-by-4.0Jan 2018View details →

Raw diffraction images of cypovirus polyhedra crystals containing Pd(allyl) complexes

Diffraction images of wild type cypovirus polyhedra in-vivo crystals (WTPhC) and the mutant (Δ3-PhC) related to PDB codes <a href="https://www.rcsb.org/structure/5YHA">5YHA</a> and <a href="https://www.rcsb.org/structure/5YHB">5YHB</a>, respectively (<a href="http://dx.doi.org/10.1039/c7cp06651a">Abe et al., 2017, Phys. Chem. Chem. Phys.</a>). Small-wedge (5° or 10°/crystal) datasets were collected from loop-harvested microcrystals automatically with ZOO system using EIGER X 9M detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group I23 with unit cell parameter a~103 Å. All datasets were processed and merged using KAMO pipeline with XDS. NOTE that master.h5 files were modified; see <a href="https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md">https://github.com/keitaroyam/yamtbx/blob/master/doc/eiger-en.md</a>.

opencc-by-4.0Aug 2024View details →

QC and WGS around the breakpoints of deletions in the compound heterozygous PRKN-deficient PD iPSC line FINi006-A (FI.CS.PRKNDex2/Dex5-7.@40)

BAM files from WGS on the regions of deletions in both PRKN gene alleles of the iPSC line (clone 18) derived using Sendai virus from PRKN 09/090 patient's fibroblasts

opencc-by-4.0Oct 2024View details →

Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma

This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference)     *experimental design  Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.   *extract protocol  PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.   *library construction protocol  Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.   *library strategy  scRNA-seq and scTCR-seq   *data processing step  Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.  Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).    *genome build/assembly  Alignment was performed using prebuilt Cell Ranger human reference GRCh38.   *processed data files format and content  RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format. Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.   <table> <tbody> <tr> <td> file name </td> <td> file checksum </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> da2e006d2b39485fd8cf8701742c6d77 </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> e125fc5031899bba71e1171888d78205 </td> </tr> <tr> <td> PD1CD8_160421_filtered_contig_annotations.csv </td> <td> 927241805d507204fbe9ef7045d0ccf4 </td> </tr> <tr> <td> PD1CD8_190421_filtered_contig_annotations.csv </td> <td> 8ca544d27f06e66592b567d3ab86551e </td> </tr> </tbody> </table>    <table> <tbody> <tr> <td> *processed data file </td> <td> antibodies/tags </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> none </td> </tr> <tr> <td> PD1CD8_160421_filtered_feature_bc_matrix.zip </td> <td> TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy </td> </tr> <tr> <td> PD1CD8_160421_filtered_contig_annotations.csv </td> <td> none </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> none </td> </tr> <tr> <td> PD1CD8_190421_filtered_feature_bc_matrix.zip </td> <td> TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy </td> </tr> <tr> <td> PD1CD8_190421_filtered_contig_annotations.csv </td> <td> none </td> </tr> </tbody> </table>

opencc-by-4.0Nov 2022View details →

Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls

The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects: <ul> <li>tremor_sdata.pickle --> A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213)</li> <li>tremor_gdata.pickle --> A group of 454 subjects who just self-reported their PD status</li> </ul> All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article. The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance: <table> <thead> <tr> <th scope="col">Index</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration</td> </tr> <tr> <td>1</td> <td>Dictionary, Denotes subject updrs</td> </tr> <tr> <td>2</td> <td>List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to</td> </tr> <tr> <td>3</td> <td>List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment</td> </tr> </tbody> </table>   The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY): * updrs16: scalar int The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject. * updrs20_right: scalar int in range [0, 4] The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs20_left: scalar int in range [0, 4] Same as above but for left hand. * updrs21_right: scalar int in range [0, 4] The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs21_left: scalar int in range [0, 4] Same as above but for left hand. * sp_expert: scalar int in range [0, 1] A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article. A '1' value indicates that the subject has tremor. A '0' value indicates that the subject doesn't have tremor. * pd_status: scalar int in range [0, 1] A '1' value indicates that the subject is a PD patient. A '0' value indicates that the subject is a Healthy Control Note: Each annotation value refers to the subject as a whole, and not in any one session.

opencc-by-4.0Mar 2022View details →

Solid-state NMR spectra of the complex of PD-L1 with the anti-PD-L1 fusion protein and of the mixture of PD-L1 with a non-binding mAb

Solid-state NMR spectra used in the analysis of the interaction between PD-L1 and anti-PDL1 fusion protein. Reference paper: Epitope mapping and binding assessment by solid-state NMR provide a  way for development of biologics under the Quality by Design paradigm

opencc-by-4.0Apr 2023View details →

Association of PD-L1 immunoexpression with the clinicopathological characteristics and its prognostic significance in OPMD and OSCC – Cross sectional study

Master chart for the study titled Association of PD-L1 immunoexpression with the clinicopathological characteristics and its prognostic significance in OPMD and OSCC  – Cross sectional study

opencc-by-4.0Oct 2023View details →

Data set for Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids

Dataset for the publication named: "Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids"

1454GCC: Anti-PD-1 (MK-3475) and IMiD (Pomalidomide) Combination Immunotherapy in Relapsed/Refractory Multiple Myeloma

ClinicalTrials.gov study NCT02289222. IPD Sharing: Not stated. Countries: 1. Publications: 1.

A Clinical Research Study to Determine Whether PD 0332991 May Be Effective in Treating Patients With Liver Cancer

ClinicalTrials.gov study NCT01356628. IPD Sharing: Not stated. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →

A Study To Evaluate The Safety And Efficacy Of IPX066 In Advanced Parkinson's Disease (ADVANCE-PD).

ClinicalTrials.gov study NCT00974974. IPD Sharing: NO. Countries: 8. Publications: 2.

Anti-PD-L1/TGF-beta Trap (M7824) Alone and in Combination With TriAd Vaccine and N-803 for Resectable Head and Neck Squamous Cell Carcinoma Not Associated With Human Papillomavirus Infection

ClinicalTrials.gov study NCT04247282. IPD Sharing: YES. Countries: 1. Publications: 3.

Safety, Tolerability, PK and PD of BI 655075 and Establishment of BI 655075 Dose(s) Effective to Reverse Prolongation of Blood Coagulation Time by Dabigatran

ClinicalTrials.gov study NCT01955720. IPD Sharing: Not stated. Countries: 1. Publications: 3.

A Study of LY3154207 in Participants With Dementia Due to Lewy Body Dementia (LBD) Associated With Idiopathic Parkinson's Disease (PD) or Dementia With Lewy Bodies (DLB)

ClinicalTrials.gov study NCT03305809. IPD Sharing: YES. Countries: 3. Publications: 2.

closedIPD-NOFeb 2026View details →

A Study to Evaluate the Safety, Tolerability, PK, PD, and Clinical Activity of EQ001 in Subjects With aGVHD

ClinicalTrials.gov study NCT03763318. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

A Study of Tilsotolimod in Combo With Ipilimumab vs Ipilimumab Alone in Subjects With Anti-PD-1 Refractory Melanoma

ClinicalTrials.gov study NCT03445533. IPD Sharing: NO. Countries: 11. Publications: 1.

Pramipexole Versus Placebo in Parkinson's Disease (PD) Patients With Depressive Symptoms

ClinicalTrials.gov study NCT00297778. IPD Sharing: Not stated. Countries: 13. Publications: 1.

AdvanTIG-202: Anti-PD-1 Monoclonal Antibody Tislelizumab (BGB-A317) Combined With or Without Anti-TIGIT Monoclonal Antibody Ociperlimab (BGB-A1217) in Participants With Previously Treated Recurrent or

ClinicalTrials.gov study NCT04693234. IPD Sharing: YES. Countries: 8. Publications: 1.

The Safety, Tolerability, PK and PD of GSK2586881 in Patients With Acute Lung Injury

ClinicalTrials.gov study NCT01597635. IPD Sharing: Not stated. Countries: 2. Publications: 1.

Measuring Cortisol Levels in Persons With Parkinson's (PD)

ClinicalTrials.gov study NCT02727270. IPD Sharing: YES. Countries: 1. Publications: 4.

A Study of Atezolizumab (an Engineered Anti-Programmed Death-Ligand 1 [PD-L1] Antibody) as Monotherapy or in Combination With Bevacizumab (Avastin®) Compared to Sunitinib (Sutent®) in Participants Wit

ClinicalTrials.gov study NCT01984242. IPD Sharing: Not stated. Countries: 9. Publications: 2.