Find research datasets worth reusing

Dataset of article: Residual capacity of RC beams subjected to impact loading: influence of reinforcement ductility. Part 3: Series I10

Part 3: Series I10 Results in this dataset are presented in Johansson et. al. (2024). Any results used from this open access data shall be cited to: Johansson M., Leppänen J., Andersson M. and Pettersson E. (2024). Residual Capacity of RC Beams Subjected to Impact Loading: Influence of reinforcement ductility. International Journal of Protective Structures. <a href="https://doi.org/10.1177/20414196241277221">https://doi.org/10.1177/20414196241277221</a> The presented data is based on Andersson and Pettersson (2019): Andersson M. and Pettersson E. (2019). Reinforced concrete beams subjected to drop-weight impact: Experimental study of the influence of reinforcement properties on the structural response. Department of Architecture and Civil Engineering. Gothenburg, Sweden: Chalmers University of Technology. Master’s Thesis ACEX30-19-27. --- This dataset consists of the following records: Part 1: General information Andersson and Pettersson 2019 - Reinforced Concrete Beams Subjected to Drop-Weight Impact Part 2: Series S Static tests – COM Correlate files Part 3: Series I10 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 4: Series I20 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 5: Result files

Dataset of article: Residual capacity of RC beams subjected to impact loading: influence of reinforcement ductility. Part 2: Series S

Part 2: Series S Results in this dataset are presented in Johansson et. al. (2024). Any results used from this open access data shall be cited to: Johansson M., Leppänen J., Andersson M. and Pettersson E. (2024). Residual Capacity of RC Beams Subjected to Impact Loading: Influence of reinforcement ductility. International Journal of Protective Structures. <a href="https://doi.org/10.1177/20414196241277221">https://doi.org/10.1177/20414196241277221</a> The presented data is based on Andersson and Pettersson (2019): Andersson M. and Pettersson E. (2019). Reinforced concrete beams subjected to drop-weight impact: Experimental study of the influence of reinforcement properties on the structural response. Department of Architecture and Civil Engineering. Gothenburg, Sweden: Chalmers University of Technology. Master’s Thesis ACEX30-19-27. --- This dataset consists of the following records: Part 1: General information Andersson and Pettersson 2019 - Reinforced Concrete Beams Subjected to Drop-Weight Impact Part 2: Series S Static tests – COM Correlate files Part 3: Series I10 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 4: Series I20 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 5: Result files

Dataset of article: Residual capacity of RC beams subjected to impact loading: influence of reinforcement ductility. Part 1: General information

Part 1: General information Results in this dataset are presented in Johansson et. al. (2024). Any results used from this open access data shall be cited to: Johansson M., Leppänen J., Andersson M. and Pettersson E. (2024). Residual Capacity of RC Beams Subjected to Impact Loading: Influence of reinforcement ductility. International Journal of Protective Structures. <a href="https://doi.org/10.1177/20414196241277221">https://doi.org/10.1177/20414196241277221</a> The presented data is based on Andersson and Pettersson (2019): Andersson M. and Pettersson E. (2019). Reinforced concrete beams subjected to drop-weight impact: Experimental study of the influence of reinforcement properties on the structural response. Department of Architecture and Civil Engineering. Gothenburg, Sweden: Chalmers University of Technology. Master’s Thesis ACEX30-19-27. --- This dataset consists of the following records: Part 1: General information Andersson and Pettersson 2019 - Reinforced Concrete Beams Subjected to Drop-Weight Impact Part 2: Series S Static tests – COM Correlate files Part 3: Series I10 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 4: Series I20 Static tests – COM Correlate files Impact tests – High speed images Impact tests – COM Correlate files Part 5: Result files

Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights

Acquiring musculoskeletal skills with curriculum-based reinforcement learning, Neuron 2024 Here we provide the weights of the neural network policies used for the analysis presented in our article. The archives whose names start with a number (01 - 32) correspond to the 32 curriculum steps to train the Baoding Balls policy which ranked first at the MyoChallenge 2022. The code used for the training and which can be used to test the policies can be found at https://github.com/amathislab/myochallenge. The archives hand_pose, hand_reach, pen and reorient correspond to the other policies used in the article. They were developed in the paper Latent exploration for reinforcement learning, Chiappa et al., NeurIPS 2023. They can be loaded and tested with the code at https://github.com/amathislab/lattice. The archive datasets includes three subfolders: rollouts, umap and csi. <ul> <li>The files in rollouts are the datasets of transitions resulting from the interaction between a policy and the environment. </li> <li>The files in umap are the pre-computed projections of specific subsets fo the datasets included in rollouts using UMAP.</li> <li>The files in csi report the performance of the policies described in our paper when applying Control Subspace Inactivation (CSI).</li> </ul> These datasets are necessary to run the notebooks to reproduce the paper's figures and main results, with the code at https://github.com/amathislab/MyoChallengeAnalysis If you find these weights useful, please cite: <div> <div>@article{chiappa2024acquiring, title = {Acquiring musculoskeletal skills with curriculum-based reinforcement learning}, journal = {Neuron}, volume = {112}, number = {23}, pages = {3969-3983.e5}, year = {2024}, issn = {0896-6273}, doi = {https://doi.org/10.1016/j.neuron.2024.09.002}, url = {https://www.sciencedirect.com/science/article/pii/S0896627324006500}, author = {Alberto Silvio Chiappa and Pablo Tano and Nisheet Patel and Abigaïl Ingster and Alexandre Pouget and Alexander Mathis}, keywords = {motor control, motor learning, reinforcement learning, curriculum learning, motor skills, musculoskeletal control, muscle synergies}, }</div> <div> </div> <div>@article{chiappa2024latent,</div> <div>title={Latent exploration for reinforcement learning},</div> <div>author={Chiappa, Alberto Silvio and Marin Vargas, Alessandro and Huang, Ann and Mathis, Alexander},</div> <div>journal={Advances in Neural Information Processing Systems},</div> <div>volume={36},</div> <div>year={2024}</div> <div>}</div> </div>

opencc-by-4.0Apr 2024View details →

X-ray tomography 3D image dataset of natural fibre reinforced polypropylene

Natural fibre composites have potential sustainability benefits over traditional composites, but their irregular shapes and mechanical properties require more thorough examination compared to glass or carbon fibre composites. 3D X-ray imaging allows for non-destructive examination of the structure and shape. The dataset includes 3D images obtained using micro X-ray computed tomography of natural fibre composites. The images provide valuable insights into the material's characteristics. Since there is limited open-access 3D image data on natural fibre composites, this dataset lays the groundwork for future image analysis and numerical modelling.

opencc-by-4.0May 2022View details →

Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome: Assemblerd contigs and gene annotations

Assembled contigs (Trinity) and gene annotations (Trinotate) of a reference transcriptome for the Upland Chorus Frog, Pseudacris feriarum. Data to assemble the contigs were obtained by sequencing four tissue types: Brain, eyes, testis, and somatic (liver/heart/lung/skin/muscle). Raw reads are stored in the NCBI-SRA database (BioProject PRJNA723357).

opencc-by-4.0Jan 2023View details →

Spontaneous behaviour is structured by reinforcement without explicit reward

Spontaneous behaviour is structured by reinforcement without explicit reward Repository containing datasets obtained for Markowitz, Gillis, Jay et al. 2023 Nature <a href="https://doi.org/10.1038/s41586-022-05611-2">https://doi.org/10.1038/s41586-022-05611-2</a> Github link to related analysis code: <a href="https://github.com/dattalab/dopamine-reinforces-spontaneous-behavior">https://github.com/dattalab/dopamine-reinforces-spontaneous-behavior</a> Abstract Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.

opencc-by-4.0Jan 2023View details →

Dataset for "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"

Dataset containing the results shown in Fig. 6 of "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark".

opencc-by-4.0Apr 2023View details →

Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries

The data contained herein support both the results described in the research article entitled "Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries" with the following DOI: <a href="https://doi.org/10.1039/D3TA00380A">10.1039/D3TA00380A</a>, and the corresponding supporting information.

opencc-by-4.0Sep 2022View details →

Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography

These are the data sets used for the publication https://arxiv.org/abs/2203.15413. In more detail, the DataFile.pkl files in the training_data and testing_data folders are the actual data, including diffraction patterns, the corresponding reconstructions and the used illumination probe. The folders dwcnt_training_data and dwcnt_testing_data include the data sets of the simulated double-walled carbon nanotube. The reconstruction folder includes the source code of the reconstruction algorithm ROP and the used parameter files. The results folder includes the updated weights of the network model and the reconstructed potentials for the 25 test data sets used in the comparison of the publication. The sequence_grid.npy file stores the sparse grid scanning sequence used for initialization and comparison as described in the publication. The dwcnt_sequence_grid.npy file is the corresponding file for the double-walled carbon nanotube data. If you want more information, please contact the corresponding author of the publication, at schlozma@hu-berlin.de.

openapache2.0May 2023View details →

Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening

This compressed file contains all datasets made for the validation of MUBDsyn.<ul><li>datasets_int_val: 17 cases in this folder are derived from <a href="https://github.com/jwxia2014/ULS-UDS">MUBD for GPCRs</a>. MUBDreal was made by <a href="https://github.com/jwxia2014/MUBD-DecoyMaker2.0">MUBD-DecoyMaker2.0</a> and MUBDsyn was made by <a href="https://github.com/taoshen99/MUBDsyn">MUBD-DecoyMakersyn</a>.</li><li>datasets_ext_val_classical_VS: Five cases in this folder are derived from the shared cases of MUV and DUD-E. The active sets of MUV were taken as the input to make corresponding MUBD datasets. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_SI_classical_VS: DeepCoy and TocoDecoy were used to make the datasets corresponding to the same five cases above. The data of DeepCoy was directly retrieved from <a href="https://opig.stats.ox.ac.uk/resources">DeepCoy resources at OPIG</a> while topology decoys of TocoDecoy_9W were made based on the scripts provided at <a href="https://github.com/5AGE-zhang/TocoDecoy">TocoDecoy GitHub Repository</a>. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_ML_VS: Ten cases in this folder are derived from <a href="http://nrlist.drugdesign.fr/">NRLiSt-BDB</a>. Corresponding MUBD datasets were made as described above.</li></ul>All these datasets can be used for the reproduction of validation performed in the manuscript or to benchmark various virtual screening methods.

opencc-by-4.0Dec 2022View details →

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task.   Abstract of the Paper on the Study Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study at the example of a routine task on a real particle accelerator, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study’s results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.

opencc-zeroJul 2023View details →

Dynamic robotic tracking of underwater targets using reinforcement learning

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new approaches and techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study is to investigate the use of deep reinforcement learning as a tool for range-only underwater target tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

opencc-zeroSep 2023View details →

Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds

From a baby's babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human-reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint, and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development, or when recovering from a neurological disorder to discover and learn new motor actions.

opencc-zeroSep 2023View details →

Data from: Culling-induced perturbation of social networks of wild geese reinforces rather than disrupts associations among survivors

Wildlife populations may be the subject of management interventions for disease control that can have unintended, counterproductive effects. Social structure exerts a strong influence over infectious disease transmission in addition to other characteristics of populations such as size and density that are the primary target for disease control. Social network approaches have been widely used to understand disease transmission in wildlife but rarely in the context of perturbations, such as culling, despite the likely impacts of such disturbance on social structure and disease dynamics. Here we present a 'removal' study of a free-living population of resident Canada geese Branta canadensis, a highly social species that is frequently managed by culling and can carry pathogens relevant to human and domestic animal health. We quantified social network structure and spatial behaviour before and after controlled culling of individuals during the summer moult. Culling did not substantially increase individual social connectivity. Individuals that moulted at cull sites or were formerly strongly associated with removed birds were more likely to strengthen and maintain any surviving existing associations while also forming new associations. However, the establishment of new associations was largely compensatory (with only small increases in the number and strength of connections) and occurred locally. Synthesis & applications: geese that survived the cull responded by strengthening existing social relationships and forming new, compensatory relationships with birds local to them in the network. In the short-term such compensatory adjustments to patterns of association in response to culling could facilitate pathogen transmission. But in the longer term, controlled culling of geese is unlikely to strongly influence pathogen spread and may even slow transmission into new social clusters by reducing wider mixing. When managing wildlife for disease control, in addition to changes in social network structure the prevalence of infection at the time of the cull and the mode of transmission (e.g., direct versus environmental) will also be critical determinants of disease transmission risk in perturbed populations of geese and other wild animals.

opencc-zeroOct 2023View details →

Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows

Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas.

ClinicalTrials.gov40/100

Early Clinical Outcomes of High-Purity Type I Collagen as a Biologic Reinforcement in Selected Hernia Repair Scenarios

ClinicalTrials.gov study NCT07360691. IPD Sharing: YES. Countries: 1. Publications: 7.

controlledIPD-YESFeb 2026View details →

publicOct 2022View details →

Estimation of reinforced urn processes under left-truncation and right-censoring

Open the record for dataset details and reuse information.

publicDec 2024View details →

Data from: An opponent striatal circuit for distributional reinforcement learning

Open the record for dataset details and reuse information.