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1. Replication Data for: Follicle Identification in Primate Ovaries via Machine Learning

   https://doi.org/10.48349/asu/bok3vo  

   Sluka, James P; Israels, Riley; Zelinski, Mary B; Dietrich, Suzanne W; Watanabe, Karen H (September 2025, ASU Library Research Data Repository)

   Abstract: <b>Overview:</b> <p> The number and types of follicles present in the ovary are key indicators of the reproductive health and capacity in females. This data set contains annotated H&E histology images from Rhesus (n=14), Cynomolgus (n=3) and Japanese (n=1) macaque (monkey) ovaries. The follicle images span the 6 preantral stages of primate ovarian follicle development: primordial, transitional primordial, primary, transitional primary, secondary, and multilayer. Follicle types were assigned by human experts. This data set is suitable for training machine learning algorithms to automatically identify and count follicles across these six developmental stages in ovarian histology images from non-human primates. In total, the dataset contains approximately 7,700 annotated follicles. These data were generated as part of the MOTHER-DB.org project. <p> The data are partitioned across multiple zip archives, which are described in detail below. Within these zip files, the individual sub-images, which were extracted from full size histology images, are centered on a classified follicle and are 200 by 200 pixels (138 by 138 micrometer) in size. The source histology image, follicle type, and any manipulations on the sub-image, are encoded in the folder and sub-image file names. See 'README_FileNamingConventions' for details on interpreting the folder and filenames. The zip files include folders for each of the follicle classes. Note that the individual sub-image filenames also contain the follicle class. Therefore, if desired, you can combine all of the sub-images into a single folder without losing the follicle type assignments. <p> <b>The complete data set, “MOTHER_Macaque_Monkey_Preantral_Follicles.zip.00N”: </b> <p> Within this Zip archive, individual images are partitioned in folders by follicle type and Train, Test and Validate subfolders used for training our machine learning algorithm. In addition, various image augmentations are included such as color inversion, image rotations, etc. Each annotation of a particular follicle generates a total of 48 augmentations. The set of 48 augmentations (which includes the original) for a particular annotation will always be in the same Train, Test or Validate folder. The data set also contains an extensive set of images representing non-follicle portions of the ovary. These images can be used as counter examples to the preantral follicle classifications sets. The image filenames identify the name of the full-size histology image, the follicle type, the location of the annotation in the full-size image and information about how it was augmented. The Train, Test, and Validate partition was done randomly to give partitions of 75:20:5. If desired, these three folders can be combined and the data repartitioned. <p> In total, the <b>dataset contains 1.7 million images</b> based on approximately 7,700 annotated follicles. <b>This is a large dataset at ~120GB.</b> You need to download the entire set of zip archives with the “.zip.00N” extensions, where N is a digit from 1 to 6. Each zip file is about 20GB. <b>A stable high-speed network is needed.</b> It will likely take several hours to download all six zip files. <p> Zip software will reconstruct the complete zip archive if you open the first file in the series. We have tested unpacking these multipart zip files using The Unarchiver for Mac (https://the-unarchiver.macupdate.com/) and 7-Zip for Windows and Linux (https://www.7-zip.org/download.html). <p> <b>Smaller data set that omits the Negatives, “MOTHER_Macaque_Monkey_Preantral_Follicles_NoNegatives.zip”:</b> <p> This data set omits the “Negative” images and only contains the sub-images of annotated follicles and their augmentations. The zip file contains ~370K images based on ~7,700 annotated follicles and is about 20% as large as the complete data set described above. <p> <b>Smallest data set that omits the Negatives and Augmentations, “MOTHER_Macaque_Monkey_Preantral_Follicles_NoNegatives_NoAugmentations.zip”: </b> <p> This data set omits the “Negatives” and all augmentation images and only contains the sub-images of annotated follicles. The zip file contains ~7,700 images, one for each of our expert-annotated follicles. <p> <b> Complete set of original histology images and annotations files, “MOTHER_TrainingData_HistoSlides_AnnotTables_20250812.zip”:</b> </p> <p>The “MOTHER_TrainingData_HistoSlides_AnnotTables_20250812.zip” file (3.8GB) contains paired full size histology images, and follicle annotation files. The follicle annotation files give the location and follicle type of every identified follicle in the image. All images in this dataset have a resolution of 0.69 micrometer/pixel and are in ome.tif format. The image files range in size from 130MB to 620MB each. The annotations files were output from QuPath, have a “.txt” extension, and are tab delimited text files. Each histology image file has an associated annotations file. For more information see the README_Training_Data_20250409.pdf file included in the zip file. </p> <p> <b>README File Naming Conventions:</b> </p> <p> The "README_FileNamingConventions.pdf" contains a detailed description of the naming conventions used for the folders and sub-image file names. The filenames contain all the information needed to identify the assigned follicle type, the original histology slide it was derived from, and any augmentation details. Methods: Original ovary histology images are available at https://mother-db.org Python code to generate the individual follicle subimages from annotated ovary histology sections is available in the MOTHER GitHub repository. See https://github.com/mother-db/MOTHER-DB-annotation-tools 

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2. Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs

   https://doi.org/10.15485/2561510  

   Dyer, Lee; Chambers, Jeff; Pastorello, Gilberto; Weber, Anna (January 2025, Next-Generation Ecosystem Experiments (NGEE) Tropics)

   {"Abstract":["This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package."]} 

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3. Glitch subtraction with adaptive spline -- Part 1

   https://doi.org/10.5281/zenodo.7592442  

   Mohanty, Soumya D (January 2023, Zenodo)

   This dataset contains the data used in the paper (arXiv:2301.02398) on the estimation and subtraction of glitches in gravitational wave data using an adaptive spline fitting method called SHAPES . Each .zip file corresponds to one of the glitches considered in the paper. The name of the class to which the glitch belongs (e.g., "Blip") is included in the name of the corresponding .zip file (e.g., BLIP_SHAPESRun_20221229T125928.zip). When uncompressed, each .zip file expands to a folder containing the following. An HDF5 file containing the Whitened gravitational wave (GW) strain data in which the glitch appeared. The data has been whitened using a proprietary code. The original (unwhitened) strain data file is available from gwosc.org. The name of the original data file is the part preceding the token '__dtrndWhtnBndpss' in the name of the file.A JSON file containing information pertinent to the glitch that was analyzed (e.g., start and stop indices in the whitened data time series).A set of .mat  files containing segmented estimates of the glitch as described in the paper.  A MATLAB script, plotglitch.m, has been provided that plots, for a given glitch folder name, the data segment that was analyzed in the paper. Another script, plotshapesestimate.m, plots the estimated glitch. These scripts require the JSONLab package. 

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4. SOS: Thermal IR Data. Version 1.0

   https://doi.org/10.26023/ea6s-ap37-yv10  

   Haugeneder, M; Hogan, D; Schwat, E; Lundquist, J; Gutmann, E; Vano, J; Haugeneder, Michael; michaelhaugenederslfch (January 2025, NSF NCAR Earth Observing Laboratory)

   {"Abstract":["This dataset contains data measured with the IR screen setup which is described in the readme file. To facilitate an overview of the captured situations, this dataset contains multiple short videos. The file names correspond to time in local time (Mountain Standard Time). On 2023-01-24 and 2023-02-27, local time is UTC-7h, while LT corresponds to UTC-6h for the measurements in May. Alongside with the videos, the original data is contained in a proprietary format. To open the *.irb-files, the InfraTec IRBIS 3 software is necessary. Furthermore, the file "SOS_IR_times.txt" contains a list of start and end times of all IR screen setup measurement sequences during the project. The original data is contained in a Proprietary Format which requires InfraTec IRBIS 3 software. The data have been packaged into zip files for ordering. This dataset and the individual day files are very large and only one file at a time can be ordered in an order."]} 

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5. Data from: Animal-borne acoustic recordings of African elephant vocalizations from Samburu National Reserve, Kenya

   https://doi.org/10.5061/dryad.xd2547dz3  

   Turner, Jesse; Wittemyer, George; Lédeczi‬, Ákos; Hedgecock, Will; Barnard, Gabriel; Kalmár, György (January 2026, Dryad)

   Abstract: African elephants are a species of conservation concern with complex vocal behavior. Annotated acoustic recordings of elephants can be used both for studying vocal behavior, including call rates and types, and developing acoustic detection and classification models. We present an annotated dataset of African elephant vocalizations from an affordable acoustic recorder (AudioMoth) integrated into a GPS collar deployed onto a wild female African elephant in Kenya. A total of 62 hours of animal-borne acoustic data were recorded and 1,123 vocalizations were identified including 706 high-quality rumbles that were further used in acoustic feature analysis. The average group call rate was 18.1 calls/hr (range: 0 - 104), with a change point analysis identifying three periods of declining call rate after the collaring event. Our findings offer insight into the vocal behavior of African elephants, and the data provided can be used in the development of acoustic detection models. TechnicalInfo: # Data from: Animal-borne acoustic recordings of African elephant vocalizations from Samburu National Reserve, Kenya This dataset contains audio recordings collected using an AudioMoth recorder deployed onto a GPS collar on an African elephant named Arden in Samburu, Kenya, in June 2025. ## Folder structure **audio.zip** (3.73 GB) - zipped folder containing 62 one-hour .wav files recorded at a 16 kHz sample rate. The start time of each file (local time, East Africa Time) is encoded in the filename, e.g. `Arden_20250606_170125.WAV`. The beginning of the filename indicates the name of the individual (Arden). **clips.zip** (120.8 MB) - zipped folder containing 1,123 .wav clips at the original sample rate (16 kHz), each containing one manually identified rumble extracted from the raw audio with a 0.2 s buffer added before and after the call. Each clip filename includes the source recording, the selection number of the call within that recording, the call type, and a quality indicator. **labels.zip** (52.8 KB) - contains 62 .txt annotation Raven files, one per raw audio file, with matching filenames. Three recordings had no vocalizations identified during manual review; empty annotation files are included for these so that the file counts between the raw audio and label folders match. **model.zip** (403.75 KB) - contains the automated rumble detection model output, run separately on 8 kHz audio and 4 kHz audio. Each of the two `4kHz` and `8kHz` subfolders contains: * a combined **annotation** file with one row per detection across all 62 recordings * a **summary** file with one row per recording, giving the file duration and total detection counts * a **per_file_annotations** subfolder, containing the per-recording split of the combined annotation file (one .txt file per source recording, matching the manual label filenames). These annotations can be directly opened and viewed in Raven. ## Manual annotation file format Each manual label file is a tab-delimited text file with one row per annotated call and the following columns: * **Selection** - ID number assigned to the call. Numbers may skip if a selection was removed during review. * **View** - spectrogram view label, constant across all files (Spectrogram 1). * **Channel** - recording channel, always 1 since all recordings are mono. * **Begin Time (s) / End Time (s)** - start and end time of the call, in seconds from the start of the recording. * **Low Freq (Hz) / High Freq (Hz)** - lower and upper frequency bounds of the annotation box. * **call_type** - RUM (rumble), ROAR, or ROAR-RUM (a call containing both). * **quality** - overall quality rating of the call, on a 0-4 scale (see Table 2 in the associated manuscript for the full description). * **overlap** - whether the call overlapped in time with another vocalization (Y/N). * **earflap** - presence of acoustic interference from ear flapping, coded 0-2 (see Table 2 in the associated manuscript for the full description). All annotations were made manually in Raven Pro. ## Model output format ### Annotation files `model/4kHz/audiomoth_downsampled_4kHz_annotation.txt` (1136 detections, 4 kHz audio) and `model/8kHz/audiomoth_downsampled_8kHz_annotation.txt` (1,395 detections, 8 kHz audio) contain the automated call detections from the trained model across all 62 recordings combined. These files use the same tab-delimited Raven selection table format as the manual labels, with additional columns generated by the detection pipeline: * **Selection** - ID number assigned to the detection. * **View** - spectrogram view label, constant across all rows. * **Channel** - recording channel; always 1 (mono). * **Event** - class label assigned by the model (Rumble for all detections in this dataset). * **Begin Time (s) / End Time (s)** - start and end time of the detection, in seconds from the start of the full concatenated recording stream (not the individual file). * **Low Freq (Hz) / High Freq (Hz)** - lower and upper frequency bounds of the detection. * **Begin Path** - full file path to the source audio file at the time the model was run. * **File Offset (s)** - start time of the detection, in seconds from the start of the individual audio file named in Begin File. * **Begin File** - filename of the audio file containing the start of the detection. * **Site** - recording site/device name. For this dataset, this is the name of the collared elephant (Arden). * **Begin Hour** - local hour of day (0-23) when the detection begins. * **File Start Date / Begin Date** - calendar date (local time) of the file start and of the detection. * **Score** - model confidence score for the detection. * **Count, Measurable, Harmonics, Ambiguous, Notes, Analyst, Rand, Deployment, Sound Problems, Call Criteria, Disk** - fields carried over from the Raven annotation template; left blank in the automated output and not used for the model results. ### Summary files `model/4kHz/audiomoth_downsampled_4kHz_summary.txt` and `model/8kHz/audiomoth_downsampled_8kHz_summary.txt` each contain one row per source recording, with the following tab-delimited columns: * **Begin Path** - full file path to the source audio file at the time the model was run. * **File Duration (s)** - length of the recording in seconds. * **Rumble Count** - number of rumble detections in that recording. * **Gunshot Count** - number of gunshot detections in that recording (0 throughout this dataset; the model was not applied to a gunshot-annotated training set for this deployment). ### Per-file annotations `model/4kHz/per_file_annotations/` and `model/8kHz/per_file_annotations/` each contain one .txt file per source recording (e.g. `Arden_20250606_130550_annotations.txt`), giving the subset of the combined annotation file corresponding to that recording, in the same column format described above. Unlike the manual `labels` folder, recordings with zero model detections do not have a corresponding file in these subfolders (59 files for 4 kHz; 61 files for 8 kHz). 

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