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Impacted canines

This dataset includes a sample of individuals with palatal canine imapction and a sample of matched controls with no canine impaction.

Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024

Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024

QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024

QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024

opencc-by-nc-4.0May 2024View details →

Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024

<h2>Description</h2> This repository contains gunshot sound training data, testing data, and the Convolutional Neural Network (CNN) model used in our study on gunhunting patterns in Gabon. The dataset includes hundreds of audio recordings containing gunshots and other environmental sounds, their associated annotations, and the trained Koogu machine-learning model. <h2>Data Summary</h2> <ul> <li>Data Types: Gunshot sound recordings, non-gunshot sound recordings, annotations, model files. Data are in the format required for Koogu, where training and test data are contained in folders of audio files with associated folders containing the Raven Pro selection tables. </li> <li>Data Format: <ul> <li>Audio files: .wav</li> <li>Annotations: .txt</li> <li>Model: TensorFlow/Keras model (.h5)</li> <li>Code: .py</li> </ul> </li> </ul> <h2>Data Details</h2> Training and Test Data: <ul> <li>train_annotations <ul> <li>"Training_Gunshots_Batch1.txt" is a Raven Pro selection table that contains 203 gunshot annotations that were detected through the spectral cross-correlation analysis.  </li> <li>"Training_Gunshots_Batch2.txt" is a Raven Pro selection table that contains 223 gunshot annotations that were detected through the initial Koogu model that was run over the whole dataset.</li> <li>"Training_NotGunshots.txt" is a Raven Pro selection table that contains 8,614 non-gunshot annotations, including sounds from branch snaps, tree falls, calls of the putty-nosed monkey, ambient noise, etc.</li> </ul> </li> <li>train_audio <ul> <li>"audio_files" contains 184 audio files associated with the selection table "Training_Gunshots_Batch1.txt"</li> <li>"audio_files_second_batch" contains 203 clips associated with the selection table "Training_Gunshots_Batch2.txt". </li> <li>"other_clips" contains 9,274 clips associated with the selection table "Training_NotGunshots.txt".</li> </ul> </li> <li>test_annotations <ul> <li>"allgunshots_test_gunshots_adjusted_final_clean_20221120.txt" is a Raven Pro selection table that contains the 138 gunshot annotations associated with the files in the "test_audio" folder. This comprises the test dataset.  </li> </ul> </li> <li>test_audio <ul> <li>Contains the 131 recordings associated with the 138 gunshot annotations in "allgunshots_test_gunshots_adjusted_final_clean_20221120.txt"</li> </ul> </li> </ul> Koogu Model: <ul> <li>qDN_4x8_x4_16_Allsites_V3 <ul> <li>This is the Koogu model used in this study. It can be run using the script "Koogu_Run_Local_Python_Gabon_ModelV3.py" that you modify depending on the locations of your data and model. </li> </ul> </li> </ul> CoLab: An example Koogu CoLab page that contains the code used to train and test the model is available <a href="https://colab.research.google.com/drive/1TerOhzsCs9zSMj9uQ31HX7XPKG1XFYCt?usp=sharing">here.</a> <h2>Data Collection</h2> <ul> <li>Collection Method: BAR-LT audio recorders deployed in 110 sites within Gabonese national parks, logging concessions, and community forests.</li> <li>Time Period: Recordings from February 2021 to June 2022.</li> <li>Geographical Information: Specific coordinates provided upon request. </li> </ul> <h2>Data Preparation</h2> <ul> <li>Downsampling: Recordings were downsampled from 44.1 kHz to 4 kHz.</li> <li>Segmentation: Audio recordings split into consecutive 2.25-second segments with 1.5 seconds of overlap.</li> <li>Normalization: Waveform normalized to [-1.0, 1.0].</li> <li>Spectrogram Computation: Using a 64 ms window length and 50% overlap, trimmed to 10–1200 Hz frequency range.</li> <li>Training Inputs: Consisted of 584 positive class (gunshots) and 25842 negative class spectrograms.</li> </ul> <h2>Requirements</h2> <ul> <li>Software Requirements: Python 3.8, TensorFlow 2.5, NumPy, Pandas.</li> <li>Hardware Requirements: GPU with at least 8GB VRAM recommended.</li> </ul> <h2>References</h2> <ul> <li>For more information about this dataset and the model specifications, please consult "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" (Yoh et al. 2024)</li> </ul> <h2>How to Cite this Dataset:</h2> <ul> <li>If you use this dataset in your work, please cite it: <ul> <li>Gottesman, B. (2024). Dataset of Gunshot Sounds and Koogu Model related to "Impacts of logging, hunting, and conservation on vocalizing biodiversity in Gabon" by Yoh et al. 2024 (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11192704" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11192704</a></li> </ul> </li> </ul>

Impact crater identification results for Chang'e-4 satellite image area and landing camera image area

Here are the results of impact crater identification from Chang'e-4 satellite image data and Chang'e-4 landing camera image data. There are 99 impact craters identified in the landing camera, with diameters ranging from 1.55m to 50.87m.There are 947 impact craters identified in the satellite images, with diameters ranging from 48.37m to 2,222.34m in diameter. All impact craters are categorised into five degradation classes (A, AB, B, BC, C). Landing camera image source: <a href="https://doi.org/10.5281/zenodo.3600427" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.3600427</a> Satellite image source: https://darts.isas.jaxa.jp/planet/pdap/selene/index.html.en

Figure 3 in Assessing the impacts of nonindigenous marine macroalgae: an update of current knowledge

Figure 3: Ecological case studies by bioregion (bioregion classification based on Kelleher et al. 1995) and algal type. Macroalgal groups: brown algae (Phaeophyceae), red algae (Rhodophyta), and green algae (Chlorophyta).

Figure 1 in Assessing the impacts of nonindigenous marine macroalgae: an update of current knowledge

Figure 1: Ecological case studies by experiment type. Each study type divided by macroalgal group: brown algae (Phaeophyceae), red algae (Rhodophyta), green algae (Chlorophyta). Sur, field survey; Sur (comp), field survey comparing invaded and noninvaded sites; Sur (BACI), field survey with temporal comparisons before/after invasion; E, field experiment; Lab, laboratory experiment or assay; O, observational study.

Figure 2 in Assessing the impacts of nonindigenous marine macroalgae: an update of current knowledge

Figure 2: Ecological case studies by impact type. Each impact type divided by macroalgal group: brown algae (Phaeophyceae), red algae (Rhodophyta), green algae (Chlorophyta). SM, space monopolization; CC, change in community composition; G, genetic effects; HT, effects on higher trophic levels; TO, toxicity; HC, habitat change; (–), no significant impact shown.

Figure 4 in Assessing the impacts of nonindigenous marine macroalgae: an update of current knowledge

Figure 4: Ecological case studies by bioregion and impact type. Impact types: SM, space monopolization; CC, change in community composition; G, genetic effects; HT, effects on higher trophic levels; TO, toxicity; HC, habitat change; (–), no significant impact shown.

Impact of an Automatic Judge in Database Learning (dataset)

This is a anonymized dataset for the assesment of the impact of using an online automatic judge for free practice in a database course over four academic years. <ul> <li><code>raw_data</code> contains one directory for each group (CSE or CSE/M) of the DB course, which in turn contains one directory for each academic year, which finally contains two CSV files with the anonymized gradebook and the online judge submission submission logs for that academic year and group. There is no submission logs for the year 2019/20 where the online judge was not available. User identifiers have been converted to sequential numbers after random shuffling the original dataset to preserve the anonymity of students.</li> <li><code>megatable.csv</code> summarizes in a single table the data contained in the separate tables for each group. Most information for the analyses have been obtained from this table, which can be generated with an attached script. Student identifiers consist of the corresponding identifier in the original table prefixed by the academic year.</li> <li><code>src</code> contains several scripts that have been used to process the data, compute statistics on them, and generate the graph and tables included in the submitted paper. They have been included to facilitate the reproducibility of the results.</li> </ul>

Data for "Impacts of an active Pacific Meridional Overturning Circulation on the Pliocene climate and hydrological cycle"

Post-Processing Scripts and Data for the Paper Title: Impacts of an Active Pacific Meridional Overturning Circulation on the Pliocene Climate and Hydrological Cycle Authors: Minmin Fu, Alexey Fedorov Publication Year: 2024 Contact: minmin.fu@yale.edu Overview This repository contains the post-processing scripts and data used in the paper. It includes Jupyter notebooks for data analysis and plot generation, as well as the GCM output and utilities for adding land-sea masks and proxy sites. Repository Structure notebooks/: Jupyter notebooks for analyzing data and creating plots. data/: GCM output files for reproducing all figures in the paper. utilities/: Functions for adding the land-sea mask and proxy sites. PlioMIP2/: PlioMIP2 boundary conditions. Data The data directory contains the GCM output files necessary for reproducing all figures. Ensure you have the necessary storage space as these files may be large. Utilities The utilities directory contains utility functions, including: Land-Sea Mask Functions: Scripts to apply land-sea masks to the data. Proxy Site Functions: Scripts to integrate proxy site data into the analysis. The PlioMIP2 directory contains the boundary condition files used for the PlioMIP2 experiments.

Supporting Excel file for "Labor and Product Impact Estimation under IMACS"

This Excel file show the calculations used to create the charts and table used in paper "Impact Estimation and Product Classification for IMACS".

Temporal Dynamics and Demographic Profiles: Evaluating the Cultural Impact of Public Engagements with Civil War Shelters in Alicante (Spain)

In the context of sustainable tourism, understanding visitors' needs and preferences is crucial for planning effective strategies that promote the conservation of historical and cultural heritage. In this regard, the quantitative analysis of survey results from visitors to Civil War air-raid shelters in Alicante, Spain, emerges as a valuable tool for gaining detailed insights that enable the planning of specific visits and events. This paper aims to explore the significance of using quantitative data in developing strategies for sustainable tourism within this particular context.

Data: Impact of forest harvesting intensity and water table on biodegradability of dissolved organic carbon in boreal peat in an incubation experiment.

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Adoption of Climate-Smart Agricultural Practices and Its Impact on Crop Productivity: A Case Study of Smallholder Farmers in Nyimba District, Zambia

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opencc-zeroMay 2024View details →

dryad32/100

Predation impacts by the dominant soft bodied and durophagous predators on temperate intertidal shores

Substantial research exists on predation and its ecology. Most research has focused on durophagous fish, brachyuran crabs, and lobsters. Data are lacking, however, on soft bodied predators like anemones, and their contribution to overall levels of predation remains largely unevaluated. Here we compared predation rates of the durophagous predator, the crab Carcinus maenas and the soft bodied predator, the anemone Actinia equina on 15 intertidal shores around Anglesey, north Wales. We employed a novel approach to assess predation based on measuring faecal output from recently collected individuals and converting to food consumed using absorption efficiencies measured using potential prey species inhabiting the same shores. Anemone mean abundance was 8.21 (±0.27, s.e.), whereas for C. maenas it was 0.23± 0.02. Absorption efficiencies when fed mussel tissue, a polychaete worm, or a shrimp were 92.8%-94.0% in C. Maenas and 40.5%-95.8% in A. equina. This reflected the different feeding modes of the two predators. Unexpectedly A. equina consumed 3.5-7 times more prey than C. maenas. The consumption of larger amounts of prey by an anemone than the dominant durophagous predator has important consequences for calculating energy flows in food webs, understanding predation controls in assemblages, and potentially for wider predation trends

Vortex and MetaModel Manager input files -- Exploring impacts of declining sea-ice on ice-dependent species in the Arctic

Input files for Vortex PVA models and for MetaModel Manager used in publication: Lacy, Robert C., Kit M. Kovacs, Christian Lydersen, and Jon Aars Linking PVA models into metamodels to explore impacts of declining sea ice on ice-dependent species in the Arctic: the ringed seal, bearded seal, polar bear complex

Solar radiation estimation in West Africa: impact of dust conditions during 2021 dry season

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The Impact of Colorful Orthodontic Appliances on Children's Motivation and Treatment Dataset

The dataset is the original SPSS sav file used for analysis in "The Impact of Colorful Orthodontic Appliances on Children's Motivation and Treatment Perception: A Single-Center Cross-Sectional Study."

Hidden citations obscure true impact in science

The repo consists of the intermediate results (subGPhy10000/) for "Hidden citations obscure true impact in science" (https://academic.oup.com/pnasnexus/article/3/5/pgae155/7664049). The corresponding code is available at <a href="https://github.com/Barabasi-Lab/hidden-citation" target="_blank" rel="noopener">https://github.com/Barabasi-Lab/hidden-citation</a>.