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Alterations between debonding and post-retention: a challenge of the appropriateness of the American Board of Orthodontics index as an indicator of excellence
<p>Dataset for all analyses of the paper.</p>
Dataset and Code Accompanying "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy"
<p>Dataset and code related to the work outlined in "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy".</p> <p>File Description:</p> <ul> <li>seis_seg.h5 - trained model weights.</li> <li>topseis_temp.npy - seismic survey.</li> <li>train_demo.ipynb - training demo including synthetic modelling pipeline.</li> <li>inference_and_plot - inference and reproducing main results.</li> </ul> <p>Pre-calculated phase volume and encoded features for reproducing figures:</p> <ul> <li>unwrapped_phase_volume_full.npy.</li> <li>features_full_ds222_ps32_s222.npy.</li> <li>indices_full_ds222_ps32_s222.npy.</li> </ul>
Fig. 1 in Taeniid cestodes in Tibetan foxes (Vulpes Ferrilata) detected by copro-PCR: Applications and challenges
Fig. 1. (continued).
FIGURE 4 in Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy
FIGURE 4. NHMUK PV P1990 after complete preparation.
FIGURE 5 in Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy
FIGURE 5. Oxford University Museum specimen CM4312 Blochius longirostris, set in Synolite 0328-A-1.
Dataset for the study: Closed-boundary reflections of shallow water waves as an open challenge for physics-informed neural networks
<p>This dataset supports a study on using Physics-Informed Neural Networks (PINNs) to solve the 1D-Shallow Water Equations. It focuses on closed boundary reflection test cases, which are crucial for accurately modeling geophysical fluid dynamics in coastal regions, particularly for storm surge and flood modeling. Properly representing reflections is also essential for accurately modeling related phenomena, such as Kelvin waves and amphidromic systems, which influence coastal water levels during such events. The individual sub-datasets in NetCDF format provide the results used for one figure each. The data arrays within the datasets are named with reference to the figures for ease of comparison. <br><br>Cite as: Demir, K.T.; Logemann, K.; Greenberg, D.S. Closed-Boundary Reflections of Shallow Water Waves as an Open Challenge for Physics-Informed Neural Networks. <em>Mathematics</em> 2024, <em>12</em>, 3315. <a href="https://doi.org/10.3390/math12213315">https://doi.org/10.3390/math12213315</a></p>
NeurOST-SSH Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using only L3 SSH (not including SST).</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
Table 10 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 10. Number of forest communities, and percent of total forest area by forest community in all measured plots across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands. Agroforest was not sampled in Palau, mangrove was not sampled in Guam or CNMI, and Montane rainforest only occurs in FSM.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Communities</b></th><th><b>Lowland [80%CI]</b></th><th><b>Strand [80%CI]</b></th><th><b>Agroforest [80%CI]</b></th><th><b>Mangrove [80%CI]</b></th><th><b>Montane [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>4</td><td>39.3% [27.2-51.4%]</td><td>27.5% [17.6-37.4%]</td><td>31.7% [21.4-42%]</td><td>1.5% [0-3.4%]</td><td>-</td></tr><tr><th>FSM</th><td>5</td><td>57.5% [51.2-63.8%]</td><td>2.8% [1-4.6%]</td><td>17.9% [13.1-22.7%]</td><td>17.1% [12.2-22%]</td><td>4.6% [1.8-7.4%]</td></tr><tr><th>Palau</th><td>3</td><td>84.1% [78.1-90.1%]</td><td>4.2% [1-7.4%]</td><td>-</td><td>11.7% [6.6-16.8%]</td><td>-</td></tr><tr><th>Guam</th><td>3</td><td>94.3% [90.3-98.3%]</td><td>2.6% [0-5.2%]</td><td>3.1% [0-7.1%]</td><td>-</td><td>-</td></tr><tr><th>CNMI</th><td>3</td><td>93% [87.8-98.2%]</td><td>6.3% [1.5-10.8%]</td><td>0.7% [0-1.6%]</td><td>-</td><td>-</td></tr></tbody></table>
Table 7 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 7. Estimated percent of forest area with live canopy cover percent of greater than or equal to 50%, 80% and 90% across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Forest area with canopy cover>=50% [80%CI]</b></th><th><b>Canopy cover>=80% [80%CI]</b></th><th><b>Canopy cover>=90% [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>92.3% [87.2-97.4%]</td><td>77.4% [68.3-86.5%]</td><td>50.1% [38-62.2%]</td></tr><tr><th>FSM</th><td>90.4% [86.4-90.8%]</td><td>52.5% [46-59%]</td><td>6.4% [3.2-9.6%]</td></tr><tr><th>Palau</th><td>92.7% [88.8-96.7%]</td><td>82.3% [76.6-88%]</td><td>61.6% [53.8-69.4%]</td></tr><tr><th>Guam</th><td>90.5% [85.3-95.7%]</td><td>62.9% [54.6-71.2%]</td><td>31.4% [23.9-38.9%]</td></tr><tr><th>CNMI</th><td>81.6% [72.8-90.4%]</td><td>58.9% [48.2-69.6%]</td><td>30.8% [20-41.6%]</td></tr></tbody></table>
Table 9 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 9. Number of live tree species per plot and total number of forest plots sampled for each forest community across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Forest community</b></th><th><b># of Tree species per plot [80%CI]</b></th><th><b># of Plots</b></th></tr></tbody><tbody><tr><th>Lowland Rainforest</th><td>7.5 [7.1-7.9]</td><td>253</td></tr><tr><th>Montane Rainforest</th><td>7.5 [6-9]</td><td>4</td></tr><tr><th>Strand Forest</th><td>3.8 [3.3-4.3]</td><td>32</td></tr><tr><th>Agroforest</th><td>3.4 [3-3.8]</td><td>44</td></tr><tr><th>Mangrove</th><td>3.4 [2.7-4.1]</td><td>24</td></tr></tbody></table>
Table 8 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 8. Estimated percent of endemic tree species, and number of endemic tree species inventoried across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Endemic trees [80%CI]</b></th><th><b># of Endemic species</b></th></tr></tbody><tbody><tr><th>RMI</th><td>0%</td><td>1</td></tr><tr><th>FSM</th><td>23.6% [19.3-27.9%]</td><td>26</td></tr><tr><th>Palau</th><td>36.7% [32.4-41%]</td><td>38</td></tr><tr><th>Guam</th><td>18.4% [11.9-22.3%]</td><td>13</td></tr><tr><th>CNMI</th><td>17.1% [9.3-27.5%]</td><td>9</td></tr></tbody></table>
Table 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 1. List of Micronesia Challenge terrestrial measures, FIA data used, and analyses performed to describe each measure. BA is basal area. N/A = indicator uses different units.</p><table><tbody><tr><th><b>MC Terrestrial Measure</b></th><th><b>FIA Data Used</b></th><th><b>Area or # of Trees</b></th><th><b>% of Forest or % of Trees</b></th></tr></tbody><tbody><tr><th><i>Human disturbance</i></th><td>Disturbance (human and fire)</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Species diversity</i></th><td>Tree species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Dominant vascular plant species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Tree species: relative dominance</td><td>Square feet per acre</td><td>BA/BA of All Trees</td></tr><tr><th></th><td>% Cover of understory species</td><td>Acreage</td><td>% of Forest Area</td></tr><tr><th><i>Forest structure</i></th><td>Tree DBH (diameter at breast height)</td><td># of Trees</td><td>% by DBH class</td></tr><tr><th></th><td>Tree height</td><td># of Trees</td><td>% by Height class</td></tr><tr><th></th><td>Basal area</td><td>Square feet per acre</td><td>N/A</td></tr><tr><th></th><td>Stem density (per plot and per acre)</td><td># of Trees per plot/acre</td><td>N/A</td></tr><tr><th><i>Invasive species</i></th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Invasive vegetation subplot cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Forest cover</i></th><td>% Live canopy cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Tree abundance</i></th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Tree rank order: endemics and invasives</td><td># of Trees</td><td>% of All trees</td></tr><tr><th><i>Mangrove stem density</i></th><td>Stem density (per acre)</td><td># of Trees per acre</td><td>N/A</td></tr><tr><th><i>Mangrove basal area</i></th><td>Basal area</td><td>Square feet per acre</td><td>Relative dominance</td></tr><tr><th><i>Forest community</i></th><td>Forest community</td><td>Acreage</td><td>% of Forest area</td></tr></tbody></table>
Table 6 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 6. Percent of forest area with invasive plant species present or covered with invasive plant species, and percent of all trees that are invasive across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Invasives present [80%CI]</b></th><th><b>Covered in invasives [80%CI]</b></th><th><b>Invasive trees [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>11.4% [4.9-17.9%]</td><td>1% [0-2%]</td><td>0</td></tr><tr><th>FSM</th><td>49.2% [43.9-54.5%]</td><td>11.3% [9-13.6%]</td><td>5.4% [2.1-8.7%]</td></tr><tr><th>Palau</th><td>13.3% [9.1-17.5%]</td><td>0.9% [0.2-1.6%]</td><td>1.2% [0-2.4%]</td></tr><tr><th>Guam</th><td>85.7% [81.8-89.6%]</td><td>39.9% [34.5-45.3%]</td><td>30.1% [23.9-36.3%]</td></tr><tr><th>CNMI</th><td>84.9% [80.4-89.4%]</td><td>54.6% [44.2-65%]</td><td>43.6% [31.8-55.4%]</td></tr></tbody></table>
Table 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 2. Source of largest forest disturbance by percent of forest area disturbed, and total percent of forest area affected by all disturbance types across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Largest disturbance type</b></th><th><b>% of Forest area [80% CI]</b></th><th><b>All disturbances [80% CI]</b></th><th><b>Endemics [80%CI]</b></th><th><b>Most common understory species</b></th><th><b>% Forest area [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>Weather</td><td>4.3% [0-8.6%]</td><td>8.1% [2.3-13.9%]</td><td>0%</td><td><i>Cocos nucifera</i></td><td>11.1% [7.7-14.5%]</td></tr><tr><th>FSM</th><td>Human</td><td>21.2% [16.2-26.2%]</td><td>39.2% [33.5-45.7%]</td><td>8.3% [6.9-9.7%]</td><td><i>Hibiscus tiliaceus</i></td><td>8.1% [6.3-9.9%]</td></tr><tr><th>Palau</th><td>Weather</td><td>10.9% [5.6-16.2%]</td><td>25.1% [18.2-32%]</td><td>20.6% [17.9-23.3%]</td><td><i>Pinanga insignis</i></td><td>16.5% [13-20%]</td></tr><tr><th>Guam</th><td>Animals</td><td>29.8% [22.2-37.4%]</td><td>49.1% [40.8-57.4%]</td><td>6.9% [5-8.8%]</td><td><i>Hibiscus tiliaceus</i></td><td>9.9% [7.8-12%]</td></tr><tr><th>CNMI</th><td>Tree Disease</td><td>40.1% [33.1-47.1%]</td><td>67.7% [58.7-76.7%]</td><td>9.3% [5.8-12.8%]</td><td><i>Leucaena leucocephala</i></td><td>23.5% [17.9-29.1%]</td></tr></tbody></table>
Table 5 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
<p>Table 5. Mean tree DBH, height, stem density, and basal area for forest across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>DBH in inches [80%CI]</b></th><th><b>Height in feet [80%CI]</b></th><th><b>Stems/acre [80%CI]</b></th><th><b>Square Feet/acre [80%CI]</b></th></tr></tbody><tbody><tr><th>RMI</th><td>4 [3.3-4.7]</td><td>23.1 [20.5-25.7]</td><td>726 [556-896]</td><td>124 [112-136]</td></tr><tr><th>FSM</th><td>4.4 [4.1-4.7]</td><td>27.3 [26.3-28.3]</td><td>611 [558-664]</td><td>145 [132-158]</td></tr><tr><th>Palau</th><td>3.9 [3.8-4]</td><td>26.9 [25.7-28.1]</td><td>937 [866-1008]</td><td>143 [131-155]</td></tr><tr><th>Guam</th><td>3.1 [3-3.2]</td><td>22.7 [22.2-23.6]</td><td>1014 [885-1143]</td><td>84 [78-90]</td></tr><tr><th>CNMI</th><td>2.8 [2.5-3.1]</td><td>20.8 [19.9-21.7]</td><td>1392 [1148-1636]</td><td>84 [72-96]</td></tr></tbody></table>
Quantum Software Engineering: Roadmap and Challenges Ahead - Supplementary material
<p>This supplementary material corresponds to the study "<em>Quantum Software Engineering: Roadmap and Challenges Ahead</em>" for the Special Issue Roadmap to 2030 by the ACM Transactions on Software Engineering and Methodology (TOSEM).</p> <p>In this supplementary material you will find the papers found in the search for the terms "<em>Quantum Software Engineering</em>" in the bibliographic databases of Scopus and Google Scholar, as well as their evolution since 2004.</p>
TABLE 2 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
<p><b>TABLE 2 |</b> Statistics of the multiple linear regression between the year of description of endemic fish species and altitude and fish body size (Cross-species analysis). P values in bold indicate significant results (p <0.05). SE = Standard error.</p><table><tbody><tr><th></th><th><b>Estimate</b></th><th><b>SE</b></th><th><b>t value</b></th><th><b>p value</b></th></tr></tbody><tbody><tr><th><b>Intercept</b></th><td>1991.44</td><td>2.378</td><td>837,28</td><td><0.001</td></tr><tr><th><b>Altitude</b></th><td>8.03</td><td>2.445</td><td>3.28</td><td><b>0.001</b></td></tr><tr><th><b>Fish body size</b></th><td>-11.05</td><td>2.445</td><td>-4.52</td><td><0.001</td></tr></tbody></table>
TABLE 1 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
<p><b>TABLE 1 |</b> Statistics of the linear regression between the number of descriptions of endemic fish species and the sampling effort (Cross-sites analysis). P value in bold indicate significant results (p <0.05). SE = Standard error.</p><table><tbody><tr><th></th><th><b>Estimate</b></th><th><b>SE</b></th><th><b>t value</b></th><th><b>p value</b></th></tr></tbody><tbody><tr><th><b>Intercept</b></th><td>-0.22</td><td>0.079</td><td>-2.88</td><td>0.004</td></tr><tr><th><b>Sampling effort</b></th><td>0.29</td><td>0.03</td><td>8.91</td><td><0.001</td></tr></tbody></table>
Helsinki Speech Challenge 2024 open audio dataset
<h1>Dataset</h1> <p>This training dataset is originally designed for the Helsinki Speech Challenge 2024 (HSC2024). While it was created with this challenge in mind, its applications extend far beyond, making it a valuable resource for developing and testing audio algorithms across diverse uses.</p> <p>Our dataset features clean speech samples generated by OpenAI's text-to-speech model, paired with corresponding recorded signals. These recorded signals are purposefully distorted by real-world effects such as filtering and reverb, offering a realistic testing ground for your audio processing algorithms.</p> <p>To ensure ease of use, the audio samples are organized into 10 separate zip files, each dedicated to a specific task and level of complexity. Most zip files include two folders clean and recorded: one containing clean audio data and the other housing the corresponding recorded (distorted) data. However, note that folders Task_3_Level_1 and Task_3_Level_2 only include recorded data, as their clean counterparts are identical to those in Task_2_Level_2 and Task_2_Level_3, respectively. Additionally, each zip file includes a .txt file with the original text samples associated with the audio clips.<br><br><strong>Update 29th October: </strong>We have now also added the test data that was used for evaluation in the challenge with similar structure to the rest of the data, contained in the zip files starting with "Test".</p> <p>Additionally, there is folder Impulse_Responses, which contains a clean sine sweep signal and short and a long white noise signal with recorded counterparts. <br>To help you get started, we've also provided an example folder containing samples from each task and level, giving you a comprehensive overview of the dataset's scope and variety.<br><br>The dataset also contains a python script evaluate.py. This can be used to evaluate the quality of audio files using the Mozilla Deepspeech speech recognition model. For more details on this script, see the more detailed description of the data challenge either on the website below or on arXiv <a href="https://arxiv.org/abs/2406.04123">https://arxiv.org/abs/2406.04123</a>.<br><br>Important dates:</p> <ul> <li>Data Challenge Launch: 10. June 2024.</li> <li>Sign-up deadline: 1. September 2024 (if you missed this deadline and wish to participate in the challenge, please send us an email).</li> <li>Submission deadline: 6. October 2024. We realize this deadline is a bit optimistic, but we humbly ask participants to try to make this deadline.</li> <li>Results are published: 4. November.</li> <li>Inverse days: 10.-13. December in Oulu, Finland.</li> </ul> <p><strong>Here is a link to the official webpage of the HSC2024: </strong><a href="https://blogs.helsinki.fi/helsinki-speech-challenge/" target="_blank" rel="noopener">https://blogs.helsinki.fi/helsinki-speech-challenge/</a><br><br><strong>Contact Email: </strong>hsc2024@helsinki.fi</p>
Business Process Intelligence Challenge 2017 - Processed
<p>This dataset is the pre-processed version of the BPIC17 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.</p>
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.