Find research datasets worth reusing

opencc-by-4.0Aug 2023View details →

Datasets for "Detecting rhythmic spiking through the power spectra of point process model residuals"

This repository contains synthetic and empirical datasets that were analyzed in the preprint "Detecting rhythmic spiking through the power spectra of point process model residuals." Relative to the previous version (1.0.0), this new version (1.0.1) only entails changes in the naming of some folders, and the text of an explanatory text file.  The changes reflect the reordering of the figures in the newest version of the manuscript.

Processed data for the manuscript: Promoting Multi-Task Learning as a General Approach for Deep-Learning-based Hydrological Models

<div> <div>Below is a brief overview of the processed data in this repository:</div> <div>- camels_streamflow: This directory contains streamflow data for CAMELS basins covering the period from January 1, 2015, to December 31, 2021. We have not included the original CAMELS dataset, which contains attributes, meteorological forcing, and streamflow data from January 1, 1980, to December 31, 2014, as it can be easily downloaded from the CAMELS website (https://gdex.ucar.edu/dataset/camels.html) and is too large for us to upload to Zenodo.</div> <div>- modiset4camels: This directory includes multiple versions of basin-mean Evapotranspiration (ET) data retrieved from the MOD16A2 data product. The dataset spans from January 1, 2001, to December 31, 2021, with an 8-day temporal resolution.</div> <div>- nldas4camels: This directory contains basin-mean daily meteorological forcing data from the NLDAS-2 dataset, obtained via Google Earth Engine (GEE). The dataset covers the period from January 1, 2001, to December 31, 2021.</div> <div>- smap4camels: This directory features basin-mean Soil Moisture (SSM) data from the NASA-USDA Enhanced SMAP Global Soil Moisture dataset, covering the period from April 2, 2015, to October 3, 2021. The dataset provides SSM measurements at a 5 cm depth. Additionally, we provide basin-mean daily SMAP L4 data spanning from April 1, 2015, to December 31, 2023.</div> </div>

Processed single cell lienage data in scLTdb

Single-cell lineage tracing (scLT) is a powerful technique that integrates DNA barcoding systems with single-cell sequencing technologies. This approach enables the simultaneous measurement of cell lineage and transcriptome or epigenomic profiles at the single-cell level. scLT technologies have been extensively employed in studying lineage relationships and cell fate heterogeneity across various biological systems. However, there is no comprehensive database available to users for searching, analyzing, visualizing, and downloading scLT data. Therefore, we developed scLTdb (scltdb.com), the first scLT database, containing 106 datasets across 36 scLT technologies. The database encompasses approximately 2.7 million cells across 13 tissues from 3 species. All datasets in scLTdb were manually curated and analyzed through a uniform workflow. scLTdb provides users with three distinct online modules designed specifically for visualizing and analyzing different modalities of scLT datasets. These modules include the Single Cell Module, which focuses on the analysis of the single cell modality; the Barcode Module, which is dedicated to the analysis of clone modality; and the Integrative Module, which facilitates the analysis of both single cell and clone modalities. Furthermore, scLTdb provides users with online tools to analyze user-provided scLT data. With these features, scLTdb aims to serve as a highly versatile platform, enabling users to conveniently explore and analyze published scLT datasets. Ultimately, scLTdb will play a vital role in promoting the advancement of modern biology.

Processed model outputs for "South Asian summer monsoon enhanced by the uplift of Iranian Plateau in Middle Miocene"

This dataset contains processed model outputs from modeling experiments performed in Zuo et al. (2024), including a set of 12 experiment with different CO2 concentrations and topography during the Middle Miocene. Due to space limitations, we provide the summer(JJA) mean climatology data for each experiment and some data that can be used to reproduce the figures in this paper. The raw data can be obtained by contacting the corresponding author. Table 1. Simulations performed with CESM1.2 in this study. <table> <tbody> <tr> <td> experiment </td> <td> Geolography </td> <td> vegetation </td> <td> CO2 (ppm) </td> <td> IP </td> <td> HM </td> </tr> <tr> <td> piControl </td> <td> Modern </td> <td> Modern </td> <td> 280 </td> <td> Modern </td> <td> Modern </td> </tr> <tr> <td> MMIO (IP100HM80) </td> <td> M.Miocene* </td> <td> M.Miocene </td> <td> 400 </td> <td> M.Miocene </td> <td> M.Miocene </td> </tr> <tr> <td> IP0HM0 </td> <td> M.Miocene </td> <td> M.Miocene </td> <td> 400 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> IP50HM0 </td> <td> M.Miocene </td> <td> M.Miocene </td> <td> 400 </td> <td> 50% </td> <td> 0 </td> </tr> <tr> <td> IP100HM0 </td> <td> M.Miocene </td> <td> M. Miocene </td> <td> 400 </td> <td> 100% </td> <td> 0 </td> </tr> <tr> <td> IP0HM100 </td> <td> M.Miocene </td> <td> M.Miocene </td> <td> 400 </td> <td> 0 </td> <td> 100%** </td> </tr> <tr> <td> IP50HM100 </td> <td> M.Miocene </td> <td> M.Miocene </td> <td> 400 </td> <td> 50% </td> <td> 100% </td> </tr> <tr> <td> IP100HM100 </td> <td> M. Miocene </td> <td> M. Miocene </td> <td> 400 </td> <td> 100% </td> <td> 100% </td> </tr> <tr> <td> MMIO280 </td> <td> M. Miocene </td> <td> M.Miocene </td> <td> 280 </td> <td> M. Miocene </td> <td> M.Miocene </td> </tr> <tr> <td> MMIO560 </td> <td> M. Miocene </td> <td> M.Miocene </td> <td> 560 </td> <td> M. Miocene </td> <td> M.Miocene </td> </tr> <tr> <td> MMIO800 </td> <td> M. Miocene </td> <td> M.Miocene </td> <td> 800 </td> <td> M. Miocene </td> <td> M.Miocene </td> </tr> <tr> <td> MMIO1000 </td> <td> M.Miocene </td> <td> M.Miocene </td> <td> 1000 </td> <td> M. Miocene </td> <td> M.Miocene </td> </tr> </tbody> </table> *M.Miocene: Middle Miocene ** 100% of the height of modern HM.

opencc-by-4.0Apr 2024View details →

Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - QEST 2024 Artefact

This artifact accompanies the QEST+FORMATS 2024 paper "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes" (<a href="https://arxiv.org/abs/2406.08175">arXiv</a>). It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the results presented in Section 5 and Appendix D. Additionally, it also contains the original raw experimental data presented in Section 5 and Appendix D and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface. Relation to paper This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is: - The toy examples presented in Example 1 and Example 2 - Table 1 in Section 5 - Table 3 in Appendix D - Figure 5 in Appendix D - Figure 6 in Appendix D - Table 4 in Appendix D Aritfact structure This artifact consists of the following files and folders: - <code>data</code>: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 5 and Appendix D. Additionally, the log files and scripts for summarizing the raw experimental data are provided. - <code>switss-multi</code>: The source code of the implementation of our presented techniques. - <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>. - <code>qest-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data. - <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image. - <code>LICENSE</code>: The license of this artifact (MIT license). - <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/). - <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it. Note on the versions: The first version (v1) contains the data and implementation at the point of the paper submission. The second version (v2) contains a Docker image and more detailed documentation and was evaluated by the QEST+FORMATS 2024 Artifact Evaluation Comittee and awarded the artifact evaluation badge. This version (v3) incorporates the feedback of the QEST+FORMATS 2024 artifact evaluation and contains improvements on the second version (v2). Acknowledgments: We would like to thank the anonymous reviewers in the QEST+FORMATS Artifact Evaluation Committee for their valuable feedback. The authors were supported by the German Federal Ministry of Education and Research (BMBF) within the project SEMECO Q1 (03ZU1210AG) and by the German Research Foundation (DFG) through the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy) and the DFG Grant 389792660 as part of TRR 248 (Foundations of Perspicuous Software System).

Code and data for Oxygen dimerization as a defect-driven process in bulk LiNiO2

Data and code required to generate figures in the article "Oxygen dimerization as a defect-driven process in bulk LiNiO2". Available as a preprint at 10.26434/chemrxiv-2024-lcmk

opencc-by-4.0Dec 2023View details →

Exploring the Molecular Pathways of the Activation Process on PPARγ Recurrent Bladder Cancer Mutants

The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in vital processes such as glucose homeostasis and adipogenesis is well established. However, its multifaceted role in cancer, particularly in luminal bladder cancer, remains a subject of intense debate. In this context, PPARγ’s overexpression and activation have been implicated in tumorigenesis. Notably, specific gain-of-function mutations (M280I, I290M, and T475M) within PPARγ’s ligand-binding domain have been pinpointed in bladder cancer, correlating with receptor activation. Nonetheless, the underlying molecular pathways prompted by these mutations remain obscure. Here, we employed a dual-basin structure-based model (db SBM) to unveil the intricate conformational dynamics that underlie the transition between PPARγ’s inactive and active states. Additionally, we explored the effects of the M280I, I290M, and T475M mutations on this pivotal process. Our findings concur with existing literature, revealing heightened ligand-independent transcriptional activity in the I290M and T475M mutants compared to other variants. Importantly, both mutants displayed enhanced stabilization of the active state relative to the wild-type receptor, and the I290M mutation promoted a remarkably specific transition route, rendering it a prime candidate for further investigation. Electrostatic analysis pinpointed the pivotal role of residues K303 and E488 in the activation cascade of I290M. This insight was substantiated by biophysical assays, as disruption of the K303-E488 interaction curtailed the thermal stabilization characteristic of the I290M mutation. These findings designate K303 and E488 as potential targets for inhibitors aimed at modulating PPARγ activation, with promising implications for refining bladder cancer prognosis. In sum, our study highlights the remarkable predictive capabilities achieved through the integration of simulation and cheminformatics methods, validated by biochemical experiments, to gain deeper insights into molecular mechanisms of activation and identify target residues for protein modulation.

The processed atmospheric wind profiles dataset in near space from ERA5 (20-50km)

<h2>experimental data</h2>

Environmental Study of Luk Ulo River Alluvial Gold Processing Use Sluice Box in Kebakalan Village, Karangayam Sub-District, Kebumen District, Central Java

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

opencc-by-4.0Feb 2024View details →

Processed Seurat objects from scRNA-seq data of the aging subventricular zone (SVZ) neurogenic niche with partial reprogramming

This repository contains the processed Seurat objects from the publication "Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche" (https://doi.org/10.1038/s43587-024-00594-3). Raw sequencing data is available at the Gene Expression Omnibus (GEO) under accession number GSE224438. Code used to process and analyze the data is available on GitHub (https://github.com/gitlucyxu/SVZreprogramming).  These Seurat objects are filtered to high-quality singlets for samples included in the publication. Descriptions and notable metadata: <ul> <li>svz_iOSKM_cohort1_toshare.rds - SVZ after whole-body partial reprogramming, cohort 1 <ul> <li>Celltype - cell type annotation</li> <li>Treatment - condition <ul> <li>untr: old control</li> <li>2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_iOSKM_cohort2_toshare.rds - SVZ after whole-body partial reprogramming, cohort 2 <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_ciOSKM_toshare.rds - SVZ after SVZ-targeted partial reprogramming <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_Dox: old+OSKM(SVZ)</li> </ul> </li> <li>MULTI_classification_rescued - mouse ID (biological replicate)</li> </ul> </li> </ul>   Updated 2024/07/01 (v2): replaced a corrupted file.

Utilization of Crude Coconut Oil as an Oil Base Mud to Overcome Differential Pipe Sticking in Drilling Processes when Penetrating Shale Formation

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

Spectral and computational dataset for "Hydrogen-atom-assisted processes on thioacetamide in para-H2 matrix – Formation of thiol tautomers"

TA-pH2_exp.zip: IR spectral data (both blank and 'real' experiments) including difference spectra, lab note files TA-pH2_calc.zip: Gaussian job and output files

DATABASE 03 School Transformation Process

Open the record for dataset details and reuse information.

Dataset for BPM2024, Educators Forum: Comprehension of (business) process models via tokens: an eye-tracking approach

Open the record for dataset details and reuse information.

opencc-by-4.0Jan 2024View details →

Replication data for: Processed food intake assortativity in the personal networks of older adults

This is the replication data for the scientific paper titled "Processed food intake assortativity in the personal networks of older adults." For details on how to use the data files, please consider the "Supplementary Material" file and the paper preprint where the context of the study and the variables of interest are presented.

FIGURE 4 in Ecomorphology of main food processing structures of four ocypodid species: how do they relate to the crabs' trophic habits?

FIGURE 4. Variations in the dorsal urocardiac teeth from the gastric mills of crabs with different diets. A, a deposit-feeder, Uca sp. (Ocypodidae); B, vascular plant-eater, Aratus pisonii (H. Milne Edwards, 1837) (Sesarmidae); C, an opportunistic carnivore/ omnivore (believed), Helice tridens (De Haan, 1835) (Varunidae). Scale = 1 mm. (A, B, after Warner 1977: 91, fig. 28; C, after Sakai et al. 2006: 6, fig. 4C).

FIGURE 3 in Ecomorphology of main food processing structures of four ocypodid species: how do they relate to the crabs' trophic habits?

FIGURE 3. Urocardiac ossicle, ventral view (A, B, C, D) and the lateral tooth of the zygocardiac ossicle (E, F, G, H) dorsal view, showing the comb-like structures of the four ocypodid species. A, E, Austruca annulipes (H. Milne Edwards, 1837); B, F, Gelasimus vocans (Linnaeus, 1758); C, G, Petruca panamensis; and D, H, Ocypode ceratophthalmus (Pallas, 1772).

FIGURE 2 in Ecomorphology of main food processing structures of four ocypodid species: how do they relate to the crabs' trophic habits?

FIGURE 2. Scatterplot and fitted lines of 'number of spoon-tipped setae' against 'carapace width' on the second maxilliped for Austruca annulipes (H. Milne Edwards, 1837), Petruca panamensis (Stimpson, 1859), Gelasimus vocans (H. Milne Edwards, 1837) and the absence of spoon-tipped setae on the second maxilliped of Ocypode ceratophthalmus (Pallas, 1772) (inset photograph).

FIGURE 1 in Ecomorphology of main food processing structures of four ocypodid species: how do they relate to the crabs' trophic habits?

FIGURE 1. Characteristics of the gastric mill of Ocypode ceratophthalmus (Pallas, 1772). A, Ventral view showing relative positions of the urocardiac ossicle and the zygocardiac ossicle; B, Dorso-lateral view of the lateral teeth of the zygocardiac ossicles, showing the comb-like structures and some details of the dentition. MT, median tooth; St, stem; U, urocardiac ossicle; Z, zygocardiac ossicle.