cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/uschist-ouc113170812
title: Daily Trojan, vol. 53, no. 34, Nov 3, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 34, November 3, 1961.
DOI: 10.25549/uschist-ouc113170820
title: Daily Trojan, vol. 53, no. 35, Nov 6, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 35, November 6, 1961.
DOI: 10.25549/uschist-ouc113170864
title: Daily Trojan, vol. 53, no. 39, Nov 10, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 39, November 10, 1961.
DOI: 10.25549/uschist-ouc113170818
title: Daily Trojan, vol. 53, no. 45, Nov 20, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 45, November 20, 1961.
DOI: 10.25549/uschist-ouc113170808
title: Daily Trojan, vol. 53, no. 48, Nov 29, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 48, November 29, 1961.
DOI: 10.25549/uschist-ouc113170952
title: Summer Trojan, vol. 11, no. 15, Aug 22, 1961

description: Southern California Summer Trojan, vol. 11, no. 15, August 22, 1961.
DOI: 10.25549/uschist-ouc113170890
title: Daily Trojan, vol. 53, no. 2, Sep 19, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 2, September 19, 1961.
DOI: 10.25549/uschist-ouc113170830
title: Daily Trojan, vol. 53, no. 18, Oct 11, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 18, October 11, 1961.
DOI: 10.25549/uschist-ouc113170870
title: Daily Trojan, vol. 53, no. 29, Oct 27, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 29, October 27, 1961.
DOI: 10.25549/uschist-ouc113170831
title: Daily Trojan, vol. 53, no. 31, Oct 31, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 31, October 31, 1961.
DOI: 10.25549/uschist-ouc113170800
title: Daily Trojan, vol. 53, no. 53, Dec 6, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 53, December 6, 1961.
DOI: 10.25549/uschist-ouc113171726
title: Daily Trojan, vol. 53, no. 73, Feb 20, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 73, February 20, 1962.
DOI: 10.25549/uschist-ouc113171450
title: Daily Trojan, vol. 53, no. 79, Feb 28, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 79, February 28, 1962.
DOI: 10.25549/uschist-ouc113171563
title: Daily Trojan, vol. 53, no. 87, Mar 13, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 87, March 13, 1962.
DOI: 10.25549/uschist-ouc113171409
title: Daily Trojan, vol. 53, no. 94, Mar 22, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 94, March 22, 1962.
DOI: 10.25549/uschist-ouc113171731
title: Daily Trojan, vol. 53, no. 74, Feb 21, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 74, February 21, 1962.
DOI: 10.25549/uschist-ouc113171629
title: Daily Trojan, vol. 53, no. 76, Feb 23, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 76, February 23, 1962.
DOI: 10.25549/uschist-ouc113171714
title: Daily Trojan, vol. 53, no. 85, Mar 8, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 85, March 8, 1962.
DOI: 10.25549/uschist-ouc113171729
title: Daily Trojan, vol. 53, no. 88, Mar 14, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 88, March 14, 1962.
DOI: 10.25549/uschist-ouc113171441
title: Daily Trojan, vol. 53, no. 90, Mar 16, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 90, March 16, 1962.
DOI: 10.25549/uschist-ouc113107250
title: Daily Trojan, vol. 52, no. 18, Oct 12, 1960

description: Southern California Daily Trojan, vol. 52, no. 13 [sic, i.e. 18], October 12, 1960.
DOI: 10.25549/uschist-ouc113107235
title: Daily Trojan, vol. 52, no. 26, Oct 24, 1960

description: Southern California Daily Trojan, vol. 52, no. 26, October 24, 1960.
DOI: 10.25549/uschist-ouc113107319
title: Daily Trojan, vol. 52, no. 35, Nov 7, 1960

description: Southern California Daily Trojan, vol. 52, no. 35, November 7, 1960.
DOI: 10.25549/uschist-ouc113107191
title: Daily Trojan, vol. 52, no. 37, Nov 9, 1960

description: Southern California Daily Trojan, vol. 52, no. 37, November 9, 1960.
DOI: 10.25549/uschist-ouc113107273
title: Daily Trojan, vol. 52, no. 44, Nov 17, 1960

description: Southern California Daily Trojan, vol. 52, no. 44, November 17, 1960.
DOI: 10.25549/uschist-ouc113170810
title: Daily Trojan, vol. 53, no. 6, Sep 25, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 6, September 25, 1961.
DOI: 10.25549/uschist-ouc113170881
title: Daily Trojan, vol. 53, no. 12, Oct 3, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 12, October 3, 1961.
DOI: 10.25549/uschist-ouc113170895
title: Daily Trojan, vol. 53, no. 15, Oct 6, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 15, October 6, 1961.
DOI: 10.25549/uschist-ouc113170839
title: Daily Trojan, vol. 53, no. 17, Oct 10, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 17, October 10, 1961.
DOI: 10.25549/uschist-ouc113170840
title: Daily Trojan, vol. 53, no. 25, Oct 20, 1961

description: University of Southern California Daily Trojan, vol. 52 [sic, i.e. 53], no. 24 [sic, i.e. 25], October 20, 1961.
DOI: 10.25549/uschist-ouc113171406
title: Daily Trojan, vol. 53, no. 100, Mar 30, 1962

description: University of Southern California Daily Trojan, vol. 53, no. 100, March 30, 1962.
DOI: 10.15468/dl.a8zk88
title: Occurrence Download

description: A dataset containing 130350 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lantana camara"
 ]
}

 The dataset includes 130350 records from 1011 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033496-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kxhedp
title: Occurrence Download

description: A dataset containing 1620 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Mushrooms Flora of Nepal at National Herbarium and Plant Laboratories (KATH)"
 ]
}

 The dataset includes 1620 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024629-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/d3qfw.v79
title: Epsilogaster fullertoni, a new species of Epsilogaster Whitfield and Mason (Hymenoptera: Braconidae: Mendesellinae) from Florida, USA
DOI: 10.48580/d3qfx.v82
title: Redescription of Libanasa brachyura Karny, 1928. (Orthoptera: Anostostomatidae:? Lutosinae) from Tanzania and problems at the subfamily level
DOI: 10.48580/d3qfz.v83
title: A new species of Troglocharinus Reitter, 1908 (Coleoptera, Leiodidae, Cholevinae, Leptodirini) from southern Catalonia, with a molecular phylogeny of the related species group
DOI: 10.48580/d3qg3.v85
title: Two new species of Amazophrynella (Amphibia: Anura: Bufonidae) from Loreto, Peru
DOI: 10.48580/d3qfp.v84
title: Description of the male of Dolomedes raptoroides Zhang, Zhu & Song, 2004 (Araneae: Lycosoidea: Pisauridae)
DOI: 10.48580/d3qfs.v84
title: A review of the Neotropical sharpshooter genus Onega Distant, 1908 (Hemiptera: Cicadellidae: Cicadellini)
DOI: 10.48580/d3qft.v86
title: Ngirhaphium Evenhuis & Grootaert from southern Thailand (Diptera: Dolichopodidae) with the description of a new species
DOI: 10.48580/d3qfv.v78
title: Two new Brazilian species of Cis Latreille, 1796 (Coleoptera: Tenebrionoidea: Ciidae)
DOI: 10.17870/bathspa.31350565.v1
title: Sonata III in G major, Berkeley Ms 791 , mvt 2, Adagio

description: Berkeley, California
DOI: 10.48580/d3qf9.v85
title: Lectotype designations in the New World Gymnetini (Coleoptera: Scarabaeidae: Cetoniinae)
DOI: 10.48580/d3qff.v76
title: Cyclacanthia n. g. (Demospongiae: Poecilosclerida: Latrunculiidae incertea sedis), a new genus of marine sponges from South African waters, and description of two new species
DOI: 10.48580/d3qfk.v89
title: New substitute name for the genus Napo Shaw, 2012 (Hymenoptera: Braconidae: Euphorinae)
DOI: 10.48580/d3qfm.v84
title: Ateuchus cujuchi n. sp., a new inquiline species of Scarabaeinae (Coleoptera: Scarabaeidae) from tuco-tuco burrows in Bolivia
DOI: 10.48580/d3qf8.v75
title: A likely microendemic new species of terrestrial iguana, genus Chalarodon, from Madagascar
DOI: 10.48580/d3qf4.v86
title: A new species of Didemnum (Ascidiacea, Tunicata) from the Atlantic coast of North America
DOI: 10.48580/d3qf5.v80
title: Status of Cyrtosia marginata Perris (Diptera: Mythicomyiidae) with remarks on the type and new distribution records
DOI: 10.48580/d3qf7.v84
title: New species and records of Melaloncha (Udamochiras) bee­killing flies (Diptera: Phoridae)
DOI: 10.48580/d3qdw.v87
title: Taxonomic revision of Elimia christyi and E. interrupta (Mollusca: Caenogastropoda: Pleuroceridae)
DOI: 10.48580/d3qdx.v80
title: Revisionary study on European species of the Empria candidata complex (Hymenoptera, Symphyta, Tenthredinidae)
DOI: 10.48580/d3qdy.v77
title: A new genus and species of Collembola from caves of south Iberian Peninsula (Collembola, Poduromorpha, Onychiuridae)
DOI: 10.48580/d3qdz.v80
title: Two new species of the genus Igerna (Hemiptera: Cicadellidae: Megophthalminae) from India
DOI: 10.48580/d3qdv.v78
title: Notozothecium janauachensis n. sp. (Monogenoidea: Dactylogyridae) from wild and cultured tambaqui, Colossoma macropomum (Teleostei: Characidae: Serrasalminae) in Brazil
DOI: 10.48580/d3qdq.v82
title: New species and records of frog-biting midges from southern Brazil (Diptera: Corethrellidae)
DOI: 10.48580/d3qdr.v78
title: Taxonomic note on four poorly known Arizona Eupithecia Curtis (Lepidoptera: Geometridae: Eupitheciini)
DOI: 10.48580/d3qds.v83
title: Simpsonichthys radiosus sp. n. (Teleostei: Cyprinodontiformes: Rivulidae): a new annual killifish from the upper Rio Tocantins basin, central Brazil
DOI: 10.48580/d3qdj.v79
title: Paguristes puniceus Henderson, 1896 (Decapoda: Anomura: Paguroidea: Diogenidae): A study in intraspecific variability
DOI: 10.48580/d3qdk.v86
title: Description of five Anthrax Scopoli puparia (Diptera, Bombyliidae, Anthracinae, Anthracini)
DOI: 10.48580/d3qdl.v81
title: Molecular and morphological assessment of Delma australis Kluge (Squamata: Pygopodidae), with a description of a new species from the biodiversity ‘ hotspot’ of southwestern Western Australia
DOI: 10.48580/d3qdn.v79
title: A new species of Indotyphlus Taylor (Amphibia: Gymnophiona: Caeciliidae) from the Western Ghats, India
DOI: 10.48580/d3qdb.v82
title: A revision of the Nearctic species of the genus Halobrecta Thomson, 1858 (Coleoptera: Staphylinidae: Aleocharinae) with notes on some Palaearctic species of the genus
DOI: 10.48580/d3qdc.v83
title: Review of the genus Sableta Casey, 1910 (Coleoptera: Staphylinidae: Aleocharinae: Athetini)
DOI: 10.48580/d3qdf.v85
title: Description of two new species of the catfish genus Trichomycterus from southeastern Brazil (Siluriformes: Trichomycteridae)
DOI: 10.48580/d3qdg.v86
title: Milnesium katarzynae sp. nov., a new species of eutardigrade (Milnesiidae) from China
DOI: 10.48580/d3qd6.v81
title: Saturnia jonasii Butler, 1877 on Jejudo Island, a new saturnid moth of South Korea with DNA data and morphology (Lepidoptera: Saturniidae)
DOI: 10.48580/d3qd4.v84
title: A new species of Corophium from eastern Australian estuaries (Crustacea: Amphipoda: Corophiidae: Corophiinae: Corophiini)
DOI: 10.48580/d3qd3.v86
title: An endemic new species of Ameiva (Squamata: Teiidae) from an isolated dry forest in southern Peru
DOI: 10.48580/d3qd5.v83
title: A review of the genus Cladotanytarsus Kieffer from China, with the description of three new species (Diptera: Chironomidae: Tanytarsini)
DOI: 10.48580/d3qd7.v79
title: Limnebius acupunctus, a new species of water beetle from Australia and Papua New Guinea (Coleoptera: Hydraenidae)
DOI: 10.17632/747n6gpz9j
title: dataset for "Deep learning identifies optimal single spectral bands for non-destructive fresh weight estimation in cabbage "

description: This dataset provides the complete code, data structure, and environment requirements necessary to reproduce the findings of the manuscript: "Deep learning identifies optimal single spectral bands for non-destructive fresh weight estimation in cabbage" (submitted to Computers and Electronics in Agriculture).

The repository is organized to support two distinct deep learning workflows, both of which are iterated over 150 separate spectral bands (band_1 to band_150):

CNN Regression: A 1D Convolutional Neural Network (CNN) to perform the main regression task (fresh weight estimation) using HDF5 (.h5) data.

Attention U-Net Segmentation: An Attention U-Net model to perform semantic segmentation on image-mask pairs, likely for plant area identification or related preprocessing.

All scripts were developed and tested using Python 3.10.

Data and Directory Structure
The project requires a BASIS_FOLDER (root directory) containing the Python scripts and a series of band_X subfolders. The data for both workflows is co-located within these band-specific folders.

1. CNN Workflow (Fresh Weight Estimation)
This workflow uses the CNN_MODEL_TRAIN_TEST_EVALUATION.py script.

BASIS_FOLDER/

band_X/

INPUT_H5/: Contains NOR_Train.h5 (H5 file with training patches and labels).

TEST_H5/: Contains NOR_Test.h5 (H5 file with test patches and labels).

MODEL_SAVE/: [Output] Directory where the trained CNN models, weights, and history logs (.csv) are saved.

RESULTS/: [Output] Directory where the final predictions (Predict_CNN_model.csv) are saved.

CNN_MODEL_TRAIN_TEST_EVALUATION.py: The main script to run this workflow.

2. Attention U-Net Workflow (Segmentation)
This workflow uses the three ATT_UNET scripts in sequence.

BASIS_FOLDER/

band_X/

input_frames/: [Input] Folder containing training images (e.g., .png, .tif).

input_masks/: [Input] Folder containing corresponding ground-truth masks.

SAVE/: [Output] Directory where the trained U-Net models, weights, and history logs (.csv) are saved.

01_ATT_UNET_MODEL.py: Script to train the U-Net model.

02_ATT_UNET_POSTPROCESSING.py: Script for post-processing and prediction.

03_ATT_UNET_EVALUATION.py: Script to evaluate the segmentation results.

Code and Reproducibility
The root folder contains the following Python scripts:

CNN_MODEL_TRAIN_TEST_EVALUATION.py: The primary script for the paper. It iterates through all bands, loads the H5 data, trains the CNN regression model, and saves all models and prediction results.

01_ATT_UNET_MODEL.py: Part 1 of the segmentation workflow. Iterates through all bands, loads image/mask pairs, and trains the Attention U-Net model.

02_ATT_UNET_POSTPROCESSING.py: Part 2 of the segmentation workflow. Loads trained models to perform prediction and post-processing tasks.

03_ATT_UNET_EVALUATION.py: Part 3 of the segmentation workflow. Evaluates the segmentation model's performance (e.g., IoU, Dice).
DOI: 10.17632/747n6gpz9j.1
title: dataset for "Deep learning identifies optimal single spectral bands for non-destructive fresh weight estimation in cabbage "

description: This dataset provides the complete code, data structure, and environment requirements necessary to reproduce the findings of the manuscript: "Deep learning identifies optimal single spectral bands for non-destructive fresh weight estimation in cabbage" (submitted to Computers and Electronics in Agriculture).

The repository is organized to support two distinct deep learning workflows, both of which are iterated over 150 separate spectral bands (band_1 to band_150):

CNN Regression: A 1D Convolutional Neural Network (CNN) to perform the main regression task (fresh weight estimation) using HDF5 (.h5) data.

Attention U-Net Segmentation: An Attention U-Net model to perform semantic segmentation on image-mask pairs, likely for plant area identification or related preprocessing.

All scripts were developed and tested using Python 3.10.

Data and Directory Structure
The project requires a BASIS_FOLDER (root directory) containing the Python scripts and a series of band_X subfolders. The data for both workflows is co-located within these band-specific folders.

1. CNN Workflow (Fresh Weight Estimation)
This workflow uses the CNN_MODEL_TRAIN_TEST_EVALUATION.py script.

BASIS_FOLDER/

band_X/

INPUT_H5/: Contains NOR_Train.h5 (H5 file with training patches and labels).

TEST_H5/: Contains NOR_Test.h5 (H5 file with test patches and labels).

MODEL_SAVE/: [Output] Directory where the trained CNN models, weights, and history logs (.csv) are saved.

RESULTS/: [Output] Directory where the final predictions (Predict_CNN_model.csv) are saved.

CNN_MODEL_TRAIN_TEST_EVALUATION.py: The main script to run this workflow.

2. Attention U-Net Workflow (Segmentation)
This workflow uses the three ATT_UNET scripts in sequence.

BASIS_FOLDER/

band_X/

input_frames/: [Input] Folder containing training images (e.g., .png, .tif).

input_masks/: [Input] Folder containing corresponding ground-truth masks.

SAVE/: [Output] Directory where the trained U-Net models, weights, and history logs (.csv) are saved.

01_ATT_UNET_MODEL.py: Script to train the U-Net model.

02_ATT_UNET_POSTPROCESSING.py: Script for post-processing and prediction.

03_ATT_UNET_EVALUATION.py: Script to evaluate the segmentation results.

Code and Reproducibility
The root folder contains the following Python scripts:

CNN_MODEL_TRAIN_TEST_EVALUATION.py: The primary script for the paper. It iterates through all bands, loads the H5 data, trains the CNN regression model, and saves all models and prediction results.

01_ATT_UNET_MODEL.py: Part 1 of the segmentation workflow. Iterates through all bands, loads image/mask pairs, and trains the Attention U-Net model.

02_ATT_UNET_POSTPROCESSING.py: Part 2 of the segmentation workflow. Loads trained models to perform prediction and post-processing tasks.

03_ATT_UNET_EVALUATION.py: Part 3 of the segmentation workflow. Evaluates the segmentation model's performance (e.g., IoU, Dice).
DOI: 10.15468/dl.4v4v73
title: Occurrence Download

description: A dataset containing 64433887 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-180 -58,180 -58,180 86,-180 86,-180 -58))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (December, November, October, September, August, July)",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year is 2018"
 ]
}

 The dataset includes 64433887 records from 3895 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019968-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17339515
title: Row data_Burrow of Scartelaos histophorus
DOI: 10.34740/kaggle/dsv/13366267
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.3535/g5q-y4g-y57
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rfw-cth-nmg
title: Porifera Grant, 1836

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.29930495
title: TWOSEASONHABITATSTUDY.csv

description: The Koklass Pheasant (Pucrasia macrolopha) is a group of ground-nesting birds of the northwestern Himalaya, remains understudied despite its wide distribution and ecological sensitivity. This study assesses its seasonal microhabitat preferences by examining four key ecologically important variables: tree structure, understory composition, ground cover, and climatic variables. Field surveys were conducted across multiple seasons, and detection data were analysed using Generalized Additive Models (GAMs) fitted with a Tweedie distribution to account for non-linear ecological responses and overdispersion. The results revealed that temperature (F = 8.60, p &lt; 0.001) had the strongest non-linear influence on capture rate (explaining 36% of deviance in capture rates), with moderate thermal conditions supporting higher detectability. Other significant predictors, i.e. among tree-layer predictors, Tree Cover had the most potent effect on capture rate (F = 5.32, p = 0.028), explaining 14.6% of deviance on capture rate. The Herb Density emerged as a significant predictor (F = 8.12, p = 0.006), explaining 13.6% of the deviance among the understory variables. The Litter Depth demonstrated a significant positive effect on capture rate (F = 6.05, p = 0.018), explaining 44.7% of deviance among ground variables. However, the Capture rates showed significant seasonal variation, with the highest values recorded in winter and spring. These findings underscore the importance of favourable and structurally complex forest habitats in supporting Koklass populations across seasons. The study offers critical insight into fine-scale habitat use and provides a scientific basis for microhabitat-based conservation strategies in Himalayan montane ecosystems.
DOI: 10.6084/m9.figshare.29930495.v1
title: TWOSEASONHABITATSTUDY.csv

description: The Koklass Pheasant (Pucrasia macrolopha) is a group of ground-nesting birds of the northwestern Himalaya, remains understudied despite its wide distribution and ecological sensitivity. This study assesses its seasonal microhabitat preferences by examining four key ecologically important variables: tree structure, understory composition, ground cover, and climatic variables. Field surveys were conducted across multiple seasons, and detection data were analysed using Generalized Additive Models (GAMs) fitted with a Tweedie distribution to account for non-linear ecological responses and overdispersion. The results revealed that temperature (F = 8.60, p &lt; 0.001) had the strongest non-linear influence on capture rate (explaining 36% of deviance in capture rates), with moderate thermal conditions supporting higher detectability. Other significant predictors, i.e. among tree-layer predictors, Tree Cover had the most potent effect on capture rate (F = 5.32, p = 0.028), explaining 14.6% of deviance on capture rate. The Herb Density emerged as a significant predictor (F = 8.12, p = 0.006), explaining 13.6% of the deviance among the understory variables. The Litter Depth demonstrated a significant positive effect on capture rate (F = 6.05, p = 0.018), explaining 44.7% of deviance among ground variables. However, the Capture rates showed significant seasonal variation, with the highest values recorded in winter and spring. These findings underscore the importance of favourable and structurally complex forest habitats in supporting Koklass populations across seasons. The study offers critical insight into fine-scale habitat use and provides a scientific basis for microhabitat-based conservation strategies in Himalayan montane ecosystems.
DOI: 10.3535/dv7-6dm-3n3
title: Bos taurus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vph-hna-pdy
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7l6-4pj-5q1
title: Cervidae Goldfuss, 1820

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zvk-xh5-cxr
title: Cetacea Brisson, 1762

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/v6k-c73-l63
title: †Palaeocarpilius A. Milne-Edwards, 1862

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.