Find research datasets worth reusing

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Poland

This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of: <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Latvia

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The Netherlands

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Slovakia

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Norway

This dataset contains the results of the surveillance activities conducted in 2021, which consisted of: <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Iceland

Schistosoma mansoni ATAC-seq results for IGV (female and male worms with and without LSD1 inhibitor)

In this study, the anti-schistosomal activity of 39 Homo sapiens Lysine Specific Demethylase 1 (HsLSD1) inhibitors was investigated on parasitic life cycle stages associated with both definitive and intermediate host infection. Amongst this collection of small molecules, compound 33 was the most potent and reduced ex vivo viabilities of schistosomula, juveniles, miracidia and adults. At its sub-lethal concentration to adults (3.13 µM), compound 33 also significantly impacted oviposition, ovarian as well as vitellarian architecture and gonadal/neoblast stem cell proliferation. ATAC-seq analysis of adults demonstrated that compound 33 significantly affected chromatin structure (intragenic regions > intergenic regions), especially in genes differentially expressed in cell populations (e.g., germinal stem cells, hes2+stem cell progeny, S1 cells and late female germinal cells) linked to these ex vivo phenotypes. The data presented here allow for visualisation in IGV https://igv.org/app/ Produced in collaboration with IHPE.

Virtual Delivery Trees Evaluation Results

The artifacts represent evaluation results of real world networks having more than 40 nodes from <a href="http://www.topology-zoo.org/">Network Topology Zoo</a>. The applied topologies are listed in following table, sorted in descending order by diameter (d) and nodal degree fluctuation (σ^2)). <table> <thead> <tr> <th scope="col">Topology</th> <th scope="col">|V|</th> <th scope="col">|E|</th> <th scope="col"><k></th> <th scope="col">σ^2</th> <th scope="col">d</th> </tr> </thead> <tbody> <tr> <td>Chinanet</td> <td>42</td> <td>66</td> <td>1.5</td> <td>10.52</td> <td>4</td> </tr> <tr> <td>Litnet</td> <td>43</td> <td>43</td> <td>0.98</td> <td>5.04</td> <td>4</td> </tr> <tr> <td>Cernet</td> <td>41</td> <td>58</td> <td>1.32</td> <td>5.6 |</td> <td>5 |</td> </tr> <tr> <td>Ntt</td> <td>32</td> <td>65</td> <td>1.48</td> <td>7.07</td> <td>6</td> </tr> <tr> <td>Cesnet200706</td> <td>44</td> <td>51</td> <td>1.16</td> <td>6.27</td> <td>6</td> </tr> <tr> <td>Carnet</td> <td>44</td> <td>43</td> <td>0.98</td> <td>5.48</td> <td>6</td> </tr> <tr> <td>Dfn</td> <td>50</td> <td>78</td> <td>1.77</td> <td>5.31</td> <td>6</td> </tr> <tr> <td>Telcove</td> <td>71</td> <td>70</td> <td>1.59</td> <td>9.13</td> <td>7</td> </tr> <tr> <td>Forthnet</td> <td>62</td> <td>62</td> <td>1.41</td> <td>7.72</td> <td>7</td> </tr> <tr> <td>Bellsouth</td> <td>51</td> <td>66</td> <td>1.5 |</td> <td>7.55 |</td> <td>7 |</td> </tr> <tr> <td>Garr200902</td> <td>54</td> <td>68</td> <td>1.55</td> <td>5.13</td> <td>7</td> </tr> <tr> <td>Arnes</td> <td>41</td> <td>57</td> <td>1.3 |</td> <td>4.53 |</td> <td>7 |</td> </tr> <tr> <td>BeyondTheNetwork</td> <td>53</td> <td>65</td> <td>1.48</td> <td>3.98</td> <td>7</td> </tr> <tr> <td>Uunet</td> <td>49</td> <td>84</td> <td>1.91</td> <td>7.38</td> <td>8</td> </tr> <tr> <td>Tw</td> <td>71</td> <td>115</td> <td>2.61</td> <td>| 5.58</td> <td>| 8</td> </tr> <tr> <td>Uninett</td> <td>71</td> <td>97</td> <td>2.2</td> <td>3.12</td> <td>9</td> </tr> <tr> <td>Renater2010</td> <td>43</td> <td>56</td> <td>1.27</td> <td>3.08</td> <td>9</td> </tr> <tr> <td>Surfnet</td> <td>50</td> <td>68</td> <td>1.55</td> <td>3.36</td> <td>11</td> </tr> <tr> <td>Iris</td> <td>51</td> <td>64</td> <td>1.45</td> <td>2.16</td> <td>11</td> </tr> <tr> <td>Palmetto</td> <td>45</td> <td>64</td> <td>1.45</td> <td>2.57</td> <td>12</td> </tr> <tr> <td>BtLatinAmerica</td> <td>45</td> <td>50</td> <td>1.14</td> <td>1.87</td> <td>12</td> </tr> <tr> <td>Bellcanada</td> <td>48</td> <td>64</td> <td>1.45</td> <td>2.59</td> <td>13</td> </tr> <tr> <td>Sanet</td> <td>43</td> <td>45</td> <td>1.02</td> <td>1.66</td> <td>13</td> </tr> <tr> <td>LambdaNet</td> <td>42</td> <td>46</td> <td>1.05</td> <td>1.57</td> <td>13</td> </tr> <tr> <td>HiberniaGlobal</td> <td>55</td> <td>81</td> <td>1.84</td> <td>2.72</td> <td>16</td> </tr> <tr> <td>Ntelos</td> <td>47</td> <td>58</td> <td>1.32</td> <td>1.92</td> <td>17</td> </tr> <tr> <td>RedBestel</td> <td>84</td> <td>93</td> <td>2.11</td> <td>0.85</td> <td>28</td> </tr> <tr> <td>VtlWavenet2008</td> <td>88</td> <td>92</td> <td>2.09</td> <td>0.11</td> <td>31</td> </tr> </tbody> </table> The evaluation results consist of three major parts: <ol> <li>Raw Data: Configuration and results of all simulation experiments as CSV files.</li> <li>Strategy Results: Visualization of the test results for each topology.</li> <li>Best Strategies: Highlighting of the best strategies across all topologies.</li> </ol> Therein, the "Raw Data" comprise the configuration of or simulation experiments and the simulation results. Each line stands for a single simulation run. "Strategy Results" and "Best Strategies" accompany the results presented in the paper. Result plots in the paper are excerpts from the plots in this repository. See below for further details. Raw Data Both, the configuration of a run and its results correspond to one line within a CSV file in subfolder <code>./raw</code>. Each file comprises the results of a replication.   <pre><code>raw ├── results_0.csv ├── results_1.csv ├── ... └── results_9.csv</code></pre> The raw data of a CSV file is structured as follows. <table> <thead> <tr> <th scope="col">Column</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>topo</td> <td>Topology name.</td> </tr> <tr> <td>peers</td> <td>Number of nodes.</td> </tr> <tr> <td>edges</td> <td>Number of links.</td> </tr> <tr> <td>p_publishers</td> <td>Proportion of nodes acting as publisher (15% - 45%).</td> </tr> <tr> <td>p_subscriber</td> <td>Proportion of nodes acting as subscriber (15% - 45%).</td> </tr> <tr> <td>n_rules</td> <td>Number of allowed rules per switch.</td> </tr> <tr> <td>distances</td> <td>Flag for consideration of geographical distances (currently not used).</td> </tr> <tr> <td>strategy</td> <td>Applied virtual tree strategy.</td> </tr> <tr> <td>distribution</td> <td>Distribution method for client (uniform, distant, nearby)</td> </tr> <tr> <td>n_cluster</td> <td>Number of simulated clusters within the topology.</td> </tr> <tr> <td>p_change</td> <td>Churn rate of clients (0% - 100%).</td> </tr> <tr> <td>pub_change</td> <td>Flag for publisher migration (currently not used).</td> </tr> <tr> <td>tree_count</td> <td>Number of virtual trees installed in the network.</td> </tr> <tr> <td>selected_subscribers</td> <td>Avg. number of subscribers addressed by a publisher</td> </tr> <tr> <td>init_cost</td> <td>Avg. number of entries of a non-optimized distribution tree (per notification)</td> </tr> <tr> <td>trees</td> <td>Avg. proportion of tree entries per notification.</td> </tr> <tr> <td>stops</td> <td>Avg. proportion of stop entries per notification.</td> </tr> <tr> <td>hops</td> <td>Avg. proportion of hop entries per notification.</td> </tr> <tr> <td>final_cost</td> <td>Aggregated proportions (trees + stops + hops).</td> </tr> <tr> <td>datetime</td> <td>Timestamp of the simulation run.</td> </tr> </tbody> </table> Result Charts The simulation results are visualized in <code>plots.md</code> or <code>plots.html</code>, ordered according above topology table. Each topology accompanys following: - Topology figures with the computed Clusters therein. - Line charts outlining the behavior of the strategies over changing Number of Flow Rules. - Bar charts outlining the strategies' performance for different Migration Scenarios. Details of the figures and diagrams are described next. <ul> <li>Clusters: Visualization of exemplary groups within the topology, computed by `clusters` and `partitions` strategy. The clusters strategy assigns 60% of a network's nodes to cluster groups; the partition strategy, in contrast, assigns all nodes to groups. Both strategies are described in Sec. III.  </li> <li>Number of Flow Rules: Results for varying number of rules (from 5 to 40) per switch, as described in Sec. IV. The charts are organized in a 3 x 3 matrix. A row of the matrix corresponds to different proportions of subscribers per publisher (15%, 30%, and 45%); a column corresponds to different distributions of clients (uniform, nearby and distant).  </li> <li>Migration Scenarios: Results for different migration scenarios with a fixed number of rules (40 rules per switch), as described in Sec. V. Each bar group stands for a strategy and reflects the results of different migration rates (0%, 30%, 50%, 70%, 100%).</li> </ul> Best Strategies Scatter plots in subfolder <code>./fluctuation</code> visualize the most efficient strategies for different migration scenarios by considering different proportions of subscribers per publisher (15%, 30%, and 45%). The plots show the results for a fixed number of subscribers (30% per publisher) and a churn rate of 100%. The strategies therein require the fewest labels in the header stack to encode a notification distribution tree, represented by the strategy's dot size

opencc-by-4.0Apr 2024View details →

Comparing recent PTA results on the nanohertz stochastic gravitational wave background - full noise and GWB parameter comparison plots

A full collection of plots comparing the noise properties of individual pulsars and gravitational wave background parameters discussed in the companion paper Comparing recent PTA results on the nanohertz stochastic gravitational wave background (IPTA 2024). <code>Section4_GWB_comparison.zip</code> supplements and expands section 4.1, "Comparing the published GWB measurements," of IPTA (2024). It contains parameter difference distributions for GWB model parameters.  There are four different models included. The HD correlated powerlaw (PL) model make up the basis for Figure 2.  Additionally, there are three comparisons not included in IPTA (2024).  First, comparisons the common uncorrelated red noise (CURN) PL model are included.  Finally,  comparisons of two free spectral (FS) models (HD and CURN) are included.  These comparisons fit the HD and CURN FS posteriors using the <code>ceffyl</code> software package, and then compare the parameters of the resulting powerlaw fits. <code>Section5_Noise_comparison.zip</code> supplements section 5, "Comparing Pulsar Noice Properties," of IPTA (2024).  It contains plots for 27 pulsars timed by more than one PTA collaboration, including the plots for PSR J1012+5307, which are presented in Figure 7.  The plots include noise parameter posteriors, time domain GP realizations, TOA residuals, and TOA radio frequency.

opencc-by-4.0Apr 2024View details →

Experiment on the performance of different machine learning algorithms for classification - Results

<h2>Results of a short performance study of machine learning algorithms</h2> <h3>Context and methodology</h3> <ul> <li>This data was produced while performing a university project to examine the performance of various machine learning algorithms on different prediction datasets</li> <li>The data serves the purpose of comparing the metrics of performing the different tasks</li> <li>The dataset contains a number of matrices for every classifier and every dataset</li> <li>The data was produced with python scripts provided further down and with the usage of the external datasets: <ul> <li>Membership Woes Dataset (OpenML): <a href="https://api.openml.org/d/44224">https://api.openml.org/d/44224</a></li> <li>Zoo dataset (UCI): <a href="https://doi.org/10.24432/C5R59V">https://doi.org/10.24432/C5R59V</a></li> <li>Breast Cancer Dataset: <a href="https://github.com/moritx/performance-experiment-machine-learning/tree/main/data">https://github.com/moritx/performance-experiment-machine-learning/tree/main/data</a></li> <li>Loan Dataset: <a href="https://github.com/moritx/performance-experiment-machine-learning/tree/main/data">https://github.com/moritx/performance-experiment-machine-learning/tree/main/data</a></li> </ul> </li> </ul> <h3>Technical details</h3> <ul> <li>The data consists of one JSON file</li> <li>The source code for producing this data is available at <a href="https://doi.org/10.5281/zenodo.11085222">https://doi.org/10.5281/zenodo.11085222</a></li> </ul> <h3>Structure of the data</h3> [ {"classifier": ..., "dataset": ..., "hyper_parameters": ..., "cross_validation_results": {     "fit_time": {} ,     "score_time": ...,     "metrics": {} },  "holdout_test_results": ...}, ]

Data on scientific production on letramento informacional in Brazil: collection procedures and resulting corpus [Dataset of thesis]

Dataset containing two dataset: 1 - Description of the data collection procedures carried out for the thesis: Appropriation of the term letramento by Brazilian Library and Information Science: terminological-conceptual tensions surrounding letramento informacional by Alves (2023). 2 - Four Spreadsheet in CSV UTF-8  containing in each of them a set of bibliographic references in partially raw data.

Results: Towards Realistic SATD Identification Through Machine Learning Models: Ongoing Research and Preliminary Results

Automated identification of self-admitted technical debt (SATD) has been crucial for advancements in managing such debt.  However, state-of-the-arts studies often overlook chronological factors, leading to experiments that do not faithfully replicate the conditions developers face in their daily routines. This study initiates a chronological analysis of SATD identification through machine learning models, emphasizing the significance of temporal factors in automated SATD detection.  The research is in its preliminary phase, divided into two stages: evaluating model performance trained on historical data and tested in prospective contexts, and examining model generalization across various projects. Preliminary results reveal that the chronological factor can positively or negatively influence model performance and that some models are not sufficiently general when trained and tested on different projects.

Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results

The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD.  This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement. Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles.  The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation.  The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP.

Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way

This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way". The results of the main five scenarios of the study are available as PyPSA result files: <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>. The results of the 330 global sensitivity analysis runs are available as summary files in CSV format: <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>

openmit-licenseApr 2024View details →

Air Quality and Exposure Disparity Results for the Bronx, New York City

This is the dataset accompanying the publication "Big Mobility Data Reveals Hyperlocal Air Pollution Exposure Disparities in the Bronx, New York". It contains mainly three parts: 1. day-to-day air quality prediction maps for exposure estimation; 2. street-level PM2.5 exposure and its disparity modeling results for all populations and for socio-demographic groups; 3. residence- and mobility-based exposure calculation for a sample of Bronx residents.

opengpl-3.0-or-laterJun 2024View details →

Model results: Model-based decision support for the choice of active spring frost protection measures in apple production

Background: Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters. As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region). Repository content: This repository contains the simulation results of 100,000 Monte Carlo runs with the model. The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.

opencc-by-4.0Jun 2024View details →

FLOW-Alaiz Benchmark: Baseline Results

This repository hosts documentation and notebooks for the <a href="https://www.flow-horizon.eu/">EU-FLOW</a> Alaiz benchmark. The first stage is finished with the publication of baseline results for PyWAsP, PyWAsP-CFD, and SiteFlow in the Torque-2024 conference. Sanz Rodrigo J, Oxley G and Tobias Olsen B (2024) FLOW-Alaiz benchmark for coupled terrain and array interaction flow models. Baseline Results. J. Phys.: Conf. Ser. 2767 092077, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/2767/9/092077">doi:10.1088/1742-6596/2767/9/092077</a>

opencc-by-4.0Jun 2024View details →

EUHFORIA modeling result of SERPENTINE event SEP-C25-0001

This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0001/">SEP-C25-0001</a>. The model includes the event CME-0001a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication: <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul>

opencc-by-4.0Dec 2023View details →

Bloume: Evaluation Results for French Benchmarks

These datasets contain the outputs of evaluation results for the BLOOM large language model tasked with a set of standard NLP benchmarks in French. The project is fully documented on github. See: https://github.com/fyvo/EvaluerBloom.