Find research datasets worth reusing

Dataset for "Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning"

Dataset for "Generalizing property prediction of ionic liquids from limited labeled data:  a one-stop framework empowered by transfer learning", codes can be found <a href="https://github.com/GuzhongChen/ILTransR">here</a>.

opencc-by-4.0Mar 2023View details →

In silico prediction and biophysical validation of novel 14-3-3σ homodimer stabilizers

this dataset is related to "In silico prediction and biophysical validation of novel 14-3-3σ homodimer stabilizers" Aljabal G., Teh, A.-H., Yap B.K.

opencc-zeroMay 2023View details →

Data from: Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.

Testability prediction dataset: Design level

This dataset contains design metrics of the software projects in the SF110 corpus. The metrics have been extracted from the extended class diagram of these projects. The last column is the design testability value of each class in the dataset. The dataset is part of the paper entitled "Measuring and improving software testability at the design level".

Trypanosoma brucei predicted protein structures, part 2 of 2

AlphaFold2-predicted protein structures for the Trypanosoma brucei (TREU927) proteome, predicted using  input multiple sequence alignments optimised for the Discoba lineage in which T. brucei sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>. This deposition contains part 2 of 2. To get the full dataset, also download TbruceiTREU927_part1.zip from <a href="https://zenodo.org/record/7940748">doi:10.5281/zenodo.7940748</a>. Data are organised with one directory per T. brucei TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find: <gene id>_predmap.png A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions. <gene id>_<start aa>-<end_aa> A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find: <gene id>_<start aa>-<end_aa>.json The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE. <gene id>_<start aa>-<end_aa>_1.pdb The PDB file of the highest ranked structure. <gene id>_<start aa>-<end_aa>_1-pae.png A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below. <gene id>_<start aa>-<end_aa>_1-plddt.png A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below. PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file. All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous. If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.

Trypanosoma brucei predicted protein structures, part 1 of 2

AlphaFold2-predicted protein structures for the Trypanosoma brucei (TREU927) proteome, predicted using  input multiple sequence alignments optimised for the Discoba lineage in which T. brucei sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>. This deposition contains part 1 of 2. To get the full dataset, also download TbruceiTREU927_part2.zip from <a href="http://zenodo.org/record/7948119">10.5281/zenodo.7948119</a>. Data are organised with one directory per T. brucei TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find: <gene id>_predmap.png A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions. <gene id>_<start aa>-<end_aa> A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find: <gene id>_<start aa>-<end_aa>.json The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE. <gene id>_<start aa>-<end_aa>_1.pdb The PDB file of the highest ranked structure. <gene id>_<start aa>-<end_aa>_1-pae.png A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below. <gene id>_<start aa>-<end_aa>_1-plddt.png A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below. PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file. All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous. If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.

Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia

Figure 1. ROC Curve of CAR, IL-6, IL-6/LY, and NLR   Figure 2. Kaplan Meier curve of (a) CAR (b) IL-6 (c) IL-6/LY (d) NLR blue line represents group above cut off and green one represents group below cut-off

Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - gaussian_fit_parameters - sample untreated_162nm

RHEED raw dataset and Gaussia fitting parameter dataset for sample "untreated_162nm" in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics

The dataset is based on the paper:  Hoang-Son Pham, Hanne Poelmans and Amr Ali-Eldin ‘’A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics’’, IEEE Access (2023). The dataset includes:  1. a list of project metadata extracted from FRIS portal 2. a list of VODS disciplines 3. a distance matrix   * Kindly refer to our paper for more details on the dataset. https://ieeexplore.ieee.org/document/10156853

Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"

Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

opencc-by-4.0Jun 2022View details →

A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset

Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>. The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).

polyGNN dataset: steel's micro/nano hardness prediction

The polyGNN dataset consists of a collection of grain-level load-depth curves corresponding to nano-indentation tests performed on a polycrystalline steel. The dataset is organized into relevant subfolders labeled as “${fmax} mN” with fmax = 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5, 3, 3.5, 4, 4.5, 5, 7, 10 denoting the maximum indentation load used in each experimental set. Every subfolder contains 15 different load-depth files with the same fmax but at different indentation points which are labeled numerically from 1 to 15 as shown in Figure 1. These files include three columns representing the indentation time (s), depth (nm), and force (mN) measurements. To reconstruct the electron backscatter diffraction (EBSD) map, the file "stal_310_s_indenty_wszystkie.ang" can be loaded into the mtex software.

Reproducibility Package for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding

This archive contains the repro-pack for the paper at <a href="http://github.com/barbagroup/jcs_paper_pinn">https://github.com/barbagroup/jcs_paper_pinn</a>.

opencc-zeroMay 2023View details →

Ecological and life-history traits predict temporal trends in biomass of boreal moths

Dramatic insect declines, and their consequences for ecosystems globally, have received considerable attention recently. Yet, it is still poorly known if ecological and life-history traits can explain declines and whether insect decline occurs also at high latitudes. Insects' diversity and abundance are dramatically lower at high latitudes compared to the tropics, and insects might benefit from climate warming in high-latitude environments. We adopted a trait- and biomass-based approach to estimate temporal change between 1993 and 2019 in Finnish macro-moth communities by using data from 85 long-running light traps. We analysed spatio-temporal variation in biomass of moth functional groups with Joint Dynamic Species Distribution Models while accounting for environmental variables. We did not detect any declining trends in total moth biomass of moth functional groups, and most groups were stable over time. Moreover, biomass increased for species using coniferous trees, lichens, or mushrooms as hosts, multivoltine species, as well as monophagous and oligophagous species feeding on trees. We found that length and temperature of the growing season, winter climatic conditions, and habitat structure all partially explained variation in moth biomass. Although boreal moth communities are rapidly changing due to species turnover, in terms of total biomass they seem to contradict the trend of dramatic insect declines observed globally. This may lessen the immediate possibility of negative bottom-up trophic cascades in boreal food webs.

MAC genome assembly and gene prediction of Tetrahymena thermophila SB210

Corrected genome assembly and gene prediction of the MAC genome of T. thermophila SB210. These data were generated and analysed in the manuscript "Single-nucleotide polymorphism landscape of the macronuclear genome of Tetrahymena thermophila". Please see the Material & methods and Supplementary data files of this manuscript for more details about these files.

Reproducibility Package (Raw Data) for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding

Raw data results from the simulations using PINN methods and PetIBM for the paper at: https://github.com/barbagroup/jcs_paper_pinn

opencc-by-4.0Nov 2022View details →

Genome annotation file containing predicted genome features of Phytophthora agathidicida (Strain: 3770, Assembly:ASM2572299v1)

This is the genome annotation file (gff3) containing predicted genome features of the Phytophthora agathidicida (Strain: 3770) genome published in Cox et al (2022). This annotation file is associated with the following entries at Genbank: Assembly:    ASM2572299v1 Biosample:   SAMN19597867 BioProject:   PRJNA734652 Included in the file are predicted functional annotations from Blastp search of all predicted proteins sequences against the Swiss-Prot sequence database (Release 23/02).

opencc-zeroJun 2023View details →

Data for: Prediction in cultured cortical neural networks

Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing, and is believed to be involved in motor and cognitive control and decision making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short and long-term memory. To answer these questions we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.

opencc-zeroJun 2023View details →

BLM overexpression as a predictive biomarker for CHK1 inhibitor response in PARP inhibitor–resistant BRCA-mutant ovarian cancer

PARP inhibitors (PARPis) have changed the treatment paradigm in BRCA-mutant high-grade serous ovarian carcinoma (HGSC). However, most patients eventually develop resistance to PARPis, highlighting an unmet need for novel therapeutic strategies. Using high-throughput drug screens, we identified ATR/CHK1 pathway inhibitors as cytotoxic, and further validated monotherapy activity of the CHK1 inhibitor (CHK1i), prexasertib, in PARPi-resistant BRCA-mutant HGSC cells and animal models. As a proof-of-concept trial, we conducted a phase II study of prexasertib in BRCA-mutant HGSC patients. The treatment was well-tolerated but yielded an objective response rate of 6% (1/17; 1 PR) in patients with prior PARPi treatment. Exploratory biomarker analyses revealed that replication stress and fork stabilization were associated with clinical benefit to CHK1i. In particular, overexpression of BLM, and CCNE1 overexpression or copy number gain/amplification were seen in patients deriving durable benefit from CHK1i. Our findings suggest replication fork–related biomarkers should be further evaluated for CHK1i sensitivity in HGSC.