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.
97
datasets available to search
ShareScore release 0.9.0
Dataset results
97 results for “Process Improvement”
Dataset of paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater"
<p>Dataset of operation of a bioelectrochemically-improved anaerobic digester (AD-BES), treating real fishery processing wastewater.<br>This dataset was used to publish the paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater" in Journal of Water Process Engineering (DOI: 10.1016/j.jwpe.2024.105848).</p>
pKaDatabase for Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge
<p>A curated a database of small molecules with experimentally measured pKa values. </p> <p>This pickle file can be loaded into memory using Pandas. In the code block below we will print out the columns of the DataFrame:</p> <pre><code class="language-python">import pandas as pd df = pd.load("pKaDatabase.pkl") print(df.keys()). # print the columns</code></pre> <blockquote> <p>['deprotonated microstate ID', 'protonated microstate ID', 'deprotonated microstate smiles', 'protonated microstate smiles', 'AM1BCC partial charge (prot. atom)', 'AM1BCC partial charge (deprot. atom)', 'AM1BCC partial charge (prot. atoms 1 bond away)', 'AM1BCC partial charge (deprot. atoms 1 bond away)', 'AM1BCC partial charge (prot. atoms 2 bond away)', 'AM1BCC partial charge (deprot. atoms 2 bond away)', 'Gasteiger partial charge (prot. atom)', 'Gasteiger partial charge (deprot. atom)', 'Gasteiger partial charge (prot. atoms 1 bond away)', 'Gasteiger partial charge (deprot. atoms 1 bond away)', 'Gasteiger partial charge (prot. atoms 2 bond away)', 'Gasteiger partial charge (deprot. atoms 2 bond away)', 'Extented Hückel partial charge (prot. atom)', 'Extented Hückel partial charge (deprot. atom)', 'Extented Hückel partial charge (prot. atoms 1 bond away)', 'Extented Hückel partial charge (deprot. atoms 1 bond away)', 'Extented Hückel partial charge (prot. atoms 2 bond away)', 'Extented Hückel partial charge (deprot. atoms 2 bond away)', '∆G_solv (kJ/mol) (prot-deprot)', 'SASA (Shrake)', 'SASA (Lee)', 'Bond Order', 'Change in Enthalpy (kJ/mol) (prot-deprot)', 'pKa','href', 'num ionizable groups', 'Weight', 'pKa source']</p> </blockquote> <p> </p> <p>For more information regarding feature calculations, please read the following paper:</p> <blockquote> <p>Raddi, Robert, and Vincent Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." (2021). <a href="https://doi.org/10.26434/chemrxiv.14650302.v1">10.26434/chemrxiv.14650302.v1</a></p> </blockquote>
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets
<p>This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).</p> <p>Content:</p> <ul> <li>dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).<br> <ul> <li> <table> <tbody> <tr> <td>Flowering</td> <td>Not Flowering</td> <td>Budding</td> <td>Not Budding</td> <td>Fruiting</td> <td>Not Fruiting</td> <td>Reproductive</td> <td>Not Reproductive</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li>data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided. <ul> <li>First metadata concerning the flowering shift and the data used to compute that value: <ul> <li> <table> <tbody> <tr> <td>genus</td> <td>genus_species</td> <td>slope</td> <td>nb_specimens</td> <td>p_value_significance</td> <td>trend_category</td> </tr> <tr> <td>Genus of the species</td> <td>Binomial name of the species</td> <td>Regression slope defining the flowering shift as a slope</td> <td>Number of herbarium specimens used to compute the shift</td> <td>P-value significance of the slope being non-zero. ('Non Significant'/'Significant')</td> <td>Summary of the shift as a binary characteristic ('Earlier'/'Later')</td> </tr> </tbody> </table> </li> </ul> </li> <li>Second, metadata summarizing various traits associated to each species: <ul> <li> <table> <tbody> <tr> <td>lifeform_status</td> <td>native_introduced_status</td> <td>wetland_status</td> <td>seasonality_average</td> <td>seasonality_spread</td> </tr> <tr> <td>Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'</td> <td>'Native'/'Introduced' status from the USDA PLANTS Database.</td> <td> <p>National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region</p> <p>'OBL'/'FACW'/'FAC'/'FACU'/'UPL'</p> </td> <td>A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'</td> <td>A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> </ul> </li> <li>phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.</li> <li>phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.</li> </ul>
Improvement of abalone hatchery and nursery production processes using organic preparation techniques
<p>France Haliotis tested the effects of different settlement cues on settlement and survival of European abalone (Haliotis tuberculata)</p>
Improving the drug discovery process by using multiple classifier systems
<p>High-quality dataset gathered from ChEMBL version 22 based on UniProt accession P34972. Regarding to activity data potential, duplicates were ignored, no activity or data validity comments were allowed, only data from binding assays and with a pCheMBL value were kept. This led to a dataset composed of 3925 chemical compounds (instances) represented using 2132 features. The first 2048 features epitomize different chemical structures fingerprints (represented using FCFP_6 notation), while the remaining 84 are associated with several physicochemical descriptors (such as Fractional Polar Surface Area, Rotatable Bonds or Molecular Weight). Finally, the set was transformed into a binary classification set where the activity cut-off was defined at a pChEMBL value > 7 and written to a tab-delimited text file. The final set contained 1977 active compounds and 1948 inactive compounds. Table 3 shows the codification of each feature grouped by type.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle's position and velocity. Each particle's movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>
The TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation dataset
<p>The combined TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (TRIPEx) was a joint field experiment of the University of Cologne, the University of Bonn, the Karlsruhe Institute of Technology (KIT), and the Research Center Jülich. TRIPEx took place at the Jülich Observatory for Cloud Evolution (JOYCE) from 11 November 2015 until 04 January 2016. During this experiment, the X-, Ka- and W-band ground-based Doppler radars were used vertically pointing to observe the clouds. Here we provide the level 2 of the dataset collected during this campaign. Several step processing were applied to minimize the radar offset and attenuation. In addition, we provide the attenuation correction and the quality flags for each processing steps to allow the user to retrieve the data without our correction. The raw and the level 1 of the dataset are available for the users on request from the corresponding author.<br> </p>
The TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (version 2)
<p>The combined TRIple-frequency and Polarimetric radar Experiment for improving process observation of winter precipitation (TRIPEx) was a joint field experiment of the University of Cologne, the University of Bonn, the Karlsruhe Institute of Technology (KIT), and the Research Center Jülich. TRIPEx took place at the Jülich Observatory for Cloud Evolution (JOYCE) from 11 November 2015 until 04 January 2016. During this experiment, the X, Ka and W Band ground-based Doppler radars were used vertically pointing to observe the clouds. Here we provide level 2 of the dataset collected during this campaign. Several step processing were applied to minimize the radar offset and attenuation. In addition, we provide the attenuation correction and the quality flags for each processing steps to allow the user to retrieve the data without our correction. The raw and the level 1 of the dataset are available for the users on request from the corresponding author.</p>
Method to Improve Workflow Net Decomposition for Process Model Repair - experiments
<p>This repository contains data that was used to carry out experiments as well as the results of these experiments.</p> <p>File names are presented in the following format: LM2-[repair method]-[b/f]-[number of experiment], where:<br> - repair method may take values "Greedy" or "Smart". "Greedy" means that the repair method of the model was greedy algorithm working with maximal decomposition. "Smart" means that the repair method was greedy algorithm as well, with the difference of decomposition method being the developed one.<br> - "b" (broken) means that the model has not undergone repair. "f" (fixed) means that the file presents a repaired model (the one which fits the initial log perfectly).<br> - number of experiment ranging from 1 to 10.</p> <p>This repository also contains the following files:<br> - LM2-CL.xes - the initial LM2 model log;<br> - LM2-CM.pnml - the initial LM2 model;<br> - LM2-Greedy-data.txt - auto-generated measurements of greedy approach performance;<br> - LM2-Smart-data.txt - auto-generated measurements of smart (developed) approach perfomance.</p>
IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data
<p>© Crown Copyright, Met Office</p> <p>This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>.</p> <p>Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.</p>
Code Blue Outcomes & Process Improvement Through Leadership Optimization Using Teleintensivists-Simulation
ClinicalTrials.gov study NCT03000829. IPD Sharing: YES. Countries: 1. Publications: 5.
Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance
Open the record for dataset details and reuse information.
Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
<p>Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and the total number of individuals harvested within an integrated band-recovery framework to estimate abundance at large scales.</p> <p>A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM.</p> <p>We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (<em>Meleagris gallapavo</em>) to estimate local and statewide abundance in Maine, USA.</p> <p>We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some sub-regions. Consistent with simulation results, this led to over-estimation of both local and statewide abundance.</p> <p>Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large-scale band recovery datasets and has applicability for the estimation of population parameters in other ecological models as well.</p>
Processed results supporting MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics
<p>Search results supporting the manuscript "MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics". Processed PSM, ion, peptide, and protein tables for each search are provided, sorted by figure within the zip file. FragPipe workflows with parameters are also provided for all searches. </p>
Improved dual-permeability model for characterizing the mass transfer process inside matrix blocks
<p>The dual-permeability model (DPM) is highly efficient for describing bimodal transport in heterogeneous porous media. However, it uses only one domain to describe the matrix blocks, and it therefore ignores the impact of the mass transfer process inside the matrix blocks at the microscale. Therefore, in this study, to investigate the effect of the mass transfer process in dual-permeability media and the computational accuracy when considering it, the dual-permeability model with a transition domain (DPMTD) is proposed based on the DPM. Comparison of the DPMTD with the DPM by applying them to a sand column experiment with the same concept as the model reveals that the DPMTD captures the bimodal transport (especially the first peak) more effectively because it calculates the rapid exchange of solute in the early stage more accurately. Subsequently, the same conclusion is reached when both models are applied to a reported solute displacement experiment for an Andisol. In short, we suggest that the mass transfer process inside matrix blocks needs to be characterized in the model to achieve higher accuracy and provides a new approach for modeling the solute transport of preferential flow.</p>
SUPPLIMANTRY [Vacuum Ohmic Heating: A Promising Technology for the Improvement of Tomato Paste Processing, Safety, Quality and Storage Stability]
<p><strong>Abstract</strong>: Ohmic heating (OH) is an electrothermal technology used to inactivate enzyme and microbial activities. This work aimed to study the impact of Ohmic Heating Under Vacuum (OHUV) which compared to conventional heating (CH) as well as storage stability at 5°C and 25 °C on microbial safety, and nutritional quality. The evaluation parameters were pH, titratable acidity, TSS, lycopene, ascorbic acid, PME, HMF, and microbiological activity. The obtained results showed that tomato paste samples that were treated by OHUV are significantly superior to CH in terms of all physicochemical and microbiological characteristics, as well as being the least harmful during storage in both transparent and dark packages. The results showed the changes in ascorbic acid, lycopene, and HMF values that were treated by OHUV at 25 °C and filled in transparent package are most affected compared to other treated samples. On the other hand, tomato paste samples stored in dark packages at 5 °C performed significantly better than those subjected to CH under the same conditions and activated PME the most had higher ascorbic acid and lycopene and fewer changes in HMF during storage time for 90 days. OHUV found to be a good alternative treatment in the production of tomato paste.</p>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
SatTerm experience: vocabulary control and facet analysis help improve the software requirement solicitation process
<p>One of the most difficult steps in the software development process is moving from requirements written in natural, uncontrolled language, to the formalisms required by the design modelling languages. To solve this issue, practitioners should pay attention to the possibility of applying vocabulary control and knowledge representation techniques to produce better specifications. The use of controlled vocabularies and the modelling of the conceptual relationships between concepts in a specific domain are expected to improve the quality of the specifications.<br> Vocabulary control and semantic modelling are promising tools to avoid the most frequent problems in the requirements specification process: lack of consistency and ambiguity. This paper provides a detailed description of the development process of an ontology used for requirements modelling in the area of satellite control systems. The process applied is based on well-established practices and guidelines applied for the construction of controlled vocabularies and faceted classifications schemas. Engineers can use the ontology when writing system specifications using predefined templates. The use of this ontology ensures the consistency of the specifications written by different engineers improves the communication with other parties involved in the system construction activities and sets the foundations for a semi-automated generation of models for subsequent design activities.</p>
Development of an Osteoarthritis (OA) Care Plan to Improve Process and Quality of OA Treatment Decisions
ClinicalTrials.gov study NCT03102580. IPD Sharing: NO. Countries: 1. Publications: 10.
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.