cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15482/usda.adc/29205824
title: Data from: Sustaining Vulnerable Agroecosystems with Compost: Lasting Benefits to Soil Health and Carbon Storage in Semiarid Winter Wheat <i>(Triticum aestivum, </i>L.)

description: Wheat agroecosystems provide one of the most important crops worldwide yet are often threatened by land degradation and climate change, making it vital to find practices that sustain soil health. Organic amendments can regenerate degraded soils, provide plant-available nutrients, and sequester carbon (C), and have long-lasting impacts in semiarid regions. This project evaluated the legacy of compost (0–50 Mg ha<sup>-1</sup>) in an organic winter wheat-fallow rotation by comparing soil health and plant growth either 1–2 or 5–6 years after compost application, with or without cover crops. The study was conducted from 2015 to 2022 at the University of Wyoming James C. Hageman Sustainable Agriculture Research & Extension Center (42°07′45.92” N, 104°23′24.88” W, 1276 m elevation) near Lingle, Wyoming. We assessed soil chemistry, structure, labile organic matter pools, microbial activity (by extracellular enzymes), and microbial community composition (by phospholipid fatty acids). We aimed to understand how compost application and cover cropping contribute to long-term soil health and C storage in semiarid regions, and how these practices impact soil microbiology. Compost resulted in sustained increases in organic matter pools, but only marginally benefitted plant growth and soil structure. Compost did not impact microbial community composition or activity, possibly because cold and dry conditions inhibited microbial functioning and compost decomposition. Cover crops did not impact soil organic matter or microbiology. Overall, we found that compost can contribute to long-term soil C storage in semiarid degraded soils such as these, despite having no measurable impacts on soil microbial biomass or activity.<br>
DOI: 10.15482/usda.adc/29205824.v1
title: Data from: Sustaining Vulnerable Agroecosystems with Compost: Lasting Benefits to Soil Health and Carbon Storage in Semiarid Winter Wheat <i>(Triticum aestivum, </i>L.)

description: Wheat agroecosystems provide one of the most important crops worldwide yet are often threatened by land degradation and climate change, making it vital to find practices that sustain soil health. Organic amendments can regenerate degraded soils, provide plant-available nutrients, and sequester carbon (C), and have long-lasting impacts in semiarid regions. This project evaluated the legacy of compost (0–50 Mg ha<sup>-1</sup>) in an organic winter wheat-fallow rotation by comparing soil health and plant growth either 1–2 or 5–6 years after compost application, with or without cover crops. The study was conducted from 2015 to 2022 at the University of Wyoming James C. Hageman Sustainable Agriculture Research & Extension Center (42°07′45.92” N, 104°23′24.88” W, 1276 m elevation) near Lingle, Wyoming. We assessed soil chemistry, structure, labile organic matter pools, microbial activity (by extracellular enzymes), and microbial community composition (by phospholipid fatty acids). We aimed to understand how compost application and cover cropping contribute to long-term soil health and C storage in semiarid regions, and how these practices impact soil microbiology. Compost resulted in sustained increases in organic matter pools, but only marginally benefitted plant growth and soil structure. Compost did not impact microbial community composition or activity, possibly because cold and dry conditions inhibited microbial functioning and compost decomposition. Cover crops did not impact soil organic matter or microbiology. Overall, we found that compost can contribute to long-term soil C storage in semiarid degraded soils such as these, despite having no measurable impacts on soil microbial biomass or activity.<br>
DOI: 10.15482/usda.adc/29631641
title: Data from: Spatiotemporal analysis of meteorological factors on dairy manure temperature during storage in an earthen pit, located in Franklin County, Virginia, U.S.A.

description: This study was conducted at a full-scale liquid manure storage pit located on a dairy farm in Franklin County, Virginia, USA. The farm milks approximately 90 cows housed in a compost-bedded pack barn. Manure from the barn alleys is scraped twice daily and deposited into an oval-shaped clay-lined earthen storage pit. The pit surface dimensions are approximately 60 m by 27 m and an average depth of 3.8 m.The study commenced on February 18, 2019, when the pit was approximately 30-40% full, and continued through seven consecutive storage periods (I to VII) over a three-year period, ending on March 31, 2022.Manure temperatures were measured using HOBO TMCx-HD sensors connected to HOBO UX120-006M 4-channel data loggers (Onset Computer Corp., Bourne, MA, USA). The data for this study were from sensors deployed at four fixed vertical heights, measured from the bottom of the pit, at 0.45 m, 0.90 m, 1.68 m, and 2.44 m. The sensors were mounted on sturdy poles installed in the manure pit, with one positioned near the inlet where the manure enters, another at the mid-section, and a third near the outlet. These sensors were submerged as the pit filled with manure, starting with 045 and continuing to the top layer at 244. The readings of a temperature sensor were only considered valid once it was completely submerged in manure. The reported temperature at any depth is an average of three readings from the sensors' locations described above.Meteorological parameters were recorded by an on-site weather station (DYACON® MS-130, DYACON Inc., Logan, UT, USA) installed on the berm of the manure storage pit. The sensors for measuring AAT, wind speed (WS), wind direction (WD), relative humidity (RH), and solar radiation (SR) were mounted approximately 3 meters above the ground. The rain gauge was installed about 1 meter above ground level. Both manure temperatures and meteorological data were recorded continuously at 30-minute intervals throughout the study.
DOI: 10.15482/usda.adc/29631641.v1
title: Data from: Spatiotemporal analysis of meteorological factors on dairy manure temperature during storage in an earthen pit, located in Franklin County, Virginia, U.S.A.

description: This study was conducted at a full-scale liquid manure storage pit located on a dairy farm in Franklin County, Virginia, USA. The farm milks approximately 90 cows housed in a compost-bedded pack barn. Manure from the barn alleys is scraped twice daily and deposited into an oval-shaped clay-lined earthen storage pit. The pit surface dimensions are approximately 60 m by 27 m and an average depth of 3.8 m.The study commenced on February 18, 2019, when the pit was approximately 30-40% full, and continued through seven consecutive storage periods (I to VII) over a three-year period, ending on March 31, 2022.Manure temperatures were measured using HOBO TMCx-HD sensors connected to HOBO UX120-006M 4-channel data loggers (Onset Computer Corp., Bourne, MA, USA). The data for this study were from sensors deployed at four fixed vertical heights, measured from the bottom of the pit, at 0.45 m, 0.90 m, 1.68 m, and 2.44 m. The sensors were mounted on sturdy poles installed in the manure pit, with one positioned near the inlet where the manure enters, another at the mid-section, and a third near the outlet. These sensors were submerged as the pit filled with manure, starting with 045 and continuing to the top layer at 244. The readings of a temperature sensor were only considered valid once it was completely submerged in manure. The reported temperature at any depth is an average of three readings from the sensors' locations described above.Meteorological parameters were recorded by an on-site weather station (DYACON® MS-130, DYACON Inc., Logan, UT, USA) installed on the berm of the manure storage pit. The sensors for measuring AAT, wind speed (WS), wind direction (WD), relative humidity (RH), and solar radiation (SR) were mounted approximately 3 meters above the ground. The rain gauge was installed about 1 meter above ground level. Both manure temperatures and meteorological data were recorded continuously at 30-minute intervals throughout the study.
DOI: 10.15482/usda.adc/25521448
title: Data and analysis scripts for: Prolonged soybean absence in the field selects for rhizobia that accumulate more polyhydroxybutyrate during symbiosis

description: During symbiosis, C that rhizobia respire to power N fixation can be stored as polyhydroxybutyrate (PHB), shown to support rhizobia survival under laboratory starvation. We collected soil in 2015 from four replicate plots per treatment in two long-term experiments at Waseca, Minnesota. Treatments differed in the intervals between soybean (<i>Glycine max</i> (L.) Merr.) hosts. We measured PHB accumulation in eight nodules per plant from four soybean (cv. ‘MN0095’) trap plants per soil sample. Trap plants were arranged in a greenhouse, common-garden experiment, and PHB accumulation was measured using flow cytometry. Treatments sampled after long intervals without soybean (greater than two years) showed a greater relative abundance of high-PHB strains. Treatments sampled after the first year of soybean following five years of a non-host crop showed a decreased relative abundance of high-PHB strains, compared to treatments sampled after long intervals without soybean. The latter result is consistent with the hypothesis (not tested directly here) that some high-PHB strains were “sanctioned” by plants as less-beneficial. Our results suggest that rhizobia strains with the tendency to allocate more C to N fixation at the expense of PHB accumulation may be less likely to persist where soybean is grown infrequently or where soil conditions make PHB particularly valuable. However, with typical two-year rotations in Minnesota, differences in PHB storage are unlikely to have a major effect on the relative survival of strains.See README.md for a detailed description of data files and scripts.
DOI: 10.15482/usda.adc/25521448.v1
title: Data and analysis scripts for: Prolonged soybean absence in the field selects for rhizobia that accumulate more polyhydroxybutyrate during symbiosis

description: During symbiosis, C that rhizobia respire to power N fixation can be stored as polyhydroxybutyrate (PHB), shown to support rhizobia survival under laboratory starvation. We collected soil in 2015 from four replicate plots per treatment in two long-term experiments at Waseca, Minnesota. Treatments differed in the intervals between soybean (<i>Glycine max</i> (L.) Merr.) hosts. We measured PHB accumulation in eight nodules per plant from four soybean (cv. ‘MN0095’) trap plants per soil sample. Trap plants were arranged in a greenhouse, common-garden experiment, and PHB accumulation was measured using flow cytometry. Treatments sampled after long intervals without soybean (greater than two years) showed a greater relative abundance of high-PHB strains. Treatments sampled after the first year of soybean following five years of a non-host crop showed a decreased relative abundance of high-PHB strains, compared to treatments sampled after long intervals without soybean. The latter result is consistent with the hypothesis (not tested directly here) that some high-PHB strains were “sanctioned” by plants as less-beneficial. Our results suggest that rhizobia strains with the tendency to allocate more C to N fixation at the expense of PHB accumulation may be less likely to persist where soybean is grown infrequently or where soil conditions make PHB particularly valuable. However, with typical two-year rotations in Minnesota, differences in PHB storage are unlikely to have a major effect on the relative survival of strains.See README.md for a detailed description of data files and scripts.
DOI: 10.34740/kaggle/ds/8793820
title: THE ARCHITECT’S FINAL CONSTANT
DOI: 10.6084/m9.figshare.28715722
title: Supplementary Data and Code from The ontogeny of decision-making in an eavesdropping predator

description: A spreadsheet and R code to reproduce the main results presented in the manuscript.
DOI: 10.6084/m9.figshare.28715722.v1
title: Supplementary Data and Code from The ontogeny of decision-making in an eavesdropping predator

description: A spreadsheet and R code to reproduce the main results presented in the manuscript.
DOI: 10.15468/dl.s6gss7
title: Occurrence Download

description: A dataset containing 259952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Urtica dioica L."
 ]
}

 The dataset includes 259952 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006534-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zrhdes
title: Occurrence Download

description: A dataset containing 422999 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-2.07567 42.1555,2.14131 42.1555,2.14131 46.94534,-2.07567 46.94534,-2.07567 42.1555))"
 ]
}

 The dataset includes 422999 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006559-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c2bagz
title: Occurrence Download

description: A dataset listing the 14285 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.42129 51.83119,-5.42727 51.44402,-4.58816 51.4378,-4.60211 51.69797,-4.76555 51.83982,-5.42129 51.83119))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 14285 records were derived from 361 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006571-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p8p5gt
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is one of (Spain, Portugal)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Vicia monardii Boiss. & Reut.",
 "Year 1950-2025"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011864-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29125727
title: BLATT-TIP

description: BLATT-TIP: a two-stage approach for accurate identification of tyrosinase inhibitory peptides based on BiLSTM and self-attention**The data file contains a dataset
DOI: 10.6084/m9.figshare.29125727.v1
title: BLATT-TIP

description: BLATT-TIP: a two-stage approach for accurate identification of tyrosinase inhibitory peptides based on BiLSTM and self-attention**The data file contains a dataset
DOI: 10.17632/w56x9jrhxr.1
title: Brain Tumor MRI Classification Dataset (Tumor vs. No Tumor)

description: Authors: Ahta Shamul Hoque Emran, Hafija Akter, Farhan Masud Nayem
Supervisor: Abdullah Al Shiam

Description:
This dataset has been developed as part of an academic thesis focused on brain tumor detection and classification using deep learning methods. It contains MRI (Magnetic Resonance Imaging) brain scans categorized into two classes: Tumor and No Tumor.
The initial dataset was imbalanced, with a significantly higher number of "No Tumor" images compared to the "Tumor" class. To address this imbalance and improve model performance, we applied data augmentation techniques to the Tumor class.

After augmentation, the dataset contains a nearly balanced number of images across both classes:
-Tumor (Original): 3,671 images
-No Tumor: 13,273 images
-Tumor (After Augmentation): 13,252 images

The total dataset used for training and analysis includes:
Tumor: 13,252 images
No Tumor: 13,273 images
All images are stored in JPG format. The size of the images in this dataset is different.

Augmentation Details:

We used TensorFlow's ImageDataGenerator for on-disk augmentation. The following parameters were applied:
- rotation_range=90: Randomly rotates images up to 90 degrees.
- rescale=1./255: Normalizes pixel values to [0, 1].
- fill_mode='nearest': Fills in missing pixels after transformation.
- Other transformations like shift, shear, zoom, and flipping were not used to retain medical image structure.

This dataset can be used for:
- Training and testing machine learning and deep learning models
- Research on medical image classification
- Developing automated systems for early detection of brain tumors
DOI: 10.17632/hx4m73v2sf.1
title: LegitPhish Dataset

description: The dataset contains 101,219 URLs and 18 features (including the label). Here's a description of each attribute: Phishing (0): 63,678 URLs

Legitimate (1): 37,540 URLs





These URLs have been sourced from the URLHaus database, scraped from many sites and other well-known repositories malicious websites actively used in phishing attacks. Each entry in this subset has been manually verified and is labeled as a phishing URL, making this dataset highly reliable for identifying harmful web content.

The legitimate URLs have been collected from reputable sources such as Wikipedia and Stack Overflow. These websites are known for hosting user-generated content and community discussions, ensuring that the URLs represent safe, legitimate web addresses. The URLs were randomly scraped to ensure diversity in the types of legitimate sites included.
Dataset Features:

URL: The full web address of each entry, providing the primary feature for analysis.
Label: A binary label indicating whether the URL is legitimate (1) or phishing (0).
Applications:

This dataset is suitable for training and evaluating machine learning models aimed at distinguishing between phishing and legitimate websites. It can be used in a variety of cybersecurity research projects, including URL-based phishing detection, web content analysis, and the development of real-time protection systems.

Usage:

Researchers can leverage this balanced dataset to develop and test algorithms for identifying phishing websites with high accuracy, using features such as URL structure, and class label attributes. The inclusion of both phishing and legitimate URLs provides a comprehensive basis for creating robust models capable of detecting phishing attempts in diverse online environments.

Feature Name Description
URL The full URL string.
url_length - Total number of characters in the URL.
has_ip_address - Binary flag (1/0): whether the URL contains an IP address.
dot_count - Number of . characters in the URL.
https_flag - Binary flag (1/0): whether the URL uses HTTPS.
url_entropy - Shannon entropy of the URL string – higher values indicate more randomness.
token_count - Number of tokens/words in the URL.
subdomain_count - Number of subdomains in the URL.
query_param_count - Number of query parameters (after ?).
tld_length - Length of the Top-Level Domain (e.g., "com" = 3).
path_length - Length of the path part after the domain.
has_hyphen_in_domain Binary flag (1/0): whether the domain contains a hyphen (-).
number_of_digits - Total number of numeric characters in the URL.
tld_popularity Binary flag (1/0): whether the TLD is popular.
suspicious_file_extension Binary flag (1/0): indicates if the URL ends with suspicious extensions (e.g., .exe, .zip).
domain_name_length - Length of the domain name.
percentage_numeric_chars - Percentage of numeric characters in the URL.
ClassLabel Target label: 1 = Legitimate, 0 = Phishing.
DOI: 10.25378/janelia.20134103
title: Near-isotropic, reconstructed volume electron microscopy (FIB-SEM) of immortalized breast cancer cell (jrc_sum159-4)

description: <b>This acquisition is part of the CellMap 2024 Segmentation Challenge</b><b>Challenge DOI:</b> https://doi.org/10.25378/janelia.c.7456966<b>Challenge Website:</b> https://cellmapchallenge.janelia.org/<b>Sample</b>: Wild-type SUM-159 cell<b>Sample Description:</b> Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structure with nanometer resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations inasmuch as they only visualize a single slice or a relatively small volume of the cell, respectively. Here, we overcome these limitations by long-term imaging whole cells and tissues via the enhanced Focus Ion Beam Scanning Electron Microscopy (FIB-SEM) platform in high resolution mode with month-long acquisition duration. We use this approach to generate reference 3D image data sets at 4-nm isotropic voxels. Together with subsequent segmentation, we hope to create a reference library to explore comprehensive quantification of whole cells and all their constituents, thus addressing questions related to cell identities, cell morphologies, cell-cell interactions, as well as intracellular organelle organization and structure.<b>Protocol</b>: High pressure freezing, freeze-substitution resin embedding with 2% OsO4 0.1% UA 3% H2O in Acetone, resin embedding in Eponate 12.<b>Contributions</b>: Sample provided by Jeeyun Chung, Tobias Walther and Bob Farese (Harvard U.), prepared for imaging by Gleb Shtengel (HHMI/Janelia), with imaging and post-processing by C. Shan Xu (HHMI/Janelia).<b>Acquisition ID</b>: jrc_sum159-4<b>Final voxel size (nm)</b>: 8.0 x 8.0 x 8.0 (X, Y, Z)<b>Dimensions (µm)</b>: 95 x 8.5 x 47.8 (X, Y, Z)<b>Imaging start date</b>: 1/19/2018<b>Imaging duration (days):</b> 6<b>Primary energy (eV)</b>: 1200<b>Imaging current (nA)</b>: 2<b>Scanning speed (MHz)</b>: .5<b>Dataset URL: </b>s3://janelia-cosem-datasets/jrc_sum159-4/jrc_sum159-4.zarr/recon-1/em/<b>Visualization Website</b>: https://openorganelle.janelia.org/datasets/jrc_sum159-4<b>Publication</b>: Xu, et al., 2017