cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/dqzkmx/xbmb13
title: 3-breast-w.csv

description: Accuracy tables for the dataset: Breast Cancer Wisconsin (Original).

Wolberg, W. (1992). Breast Cancer Wisconsin (Original). UCI Machine Learning Repository. https://doi.org/10.24432/C5HP4Z. Licensed under CC BY 4.0.
DOI: 10.18710/hmf7ee
title: Replication Data for: Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships

description: Field trials on risk-based path planning with an autonomous surface vehicle.
description: Two field trial experiments were performed to test and demonstrate risk-based path planning with KPRA, a method for dynamic probabilistic risk assessment, and risk evaluation on an autonomous surface vehicle (ASV) from Maritime Robotics. The main objective was to observe the effect of KPRA on an ASV’s behavior, compared to its nominal behavior, in an uncontrolled and uncertain environment.

The first experiment tested KPRA’s effect on the ASV navigating open waters during a crossing encounter with another vessel. The second experiment increased the environmental complexity and tested the method in a crossing encounter near an island. Both experiments yielded datasets, called "open_waters" and "near_land" respectively, containing data on the ASV's state (e.g., position, speed and heading), the other vessel's state, and the explored planning state-space (i.e., the states considered by KPRA when searching for an adequate path), among others.

Both experiments were performed in areas around Munkholmen island in Trondheim, Norway. The island is a tourist spot in Trondheim, frequently approached by recreational vessels and kayaks, so the risks of collision and grounding are present. In both experiments, the main goal for the ASV was to autonomously navigate near the island while performing risk assessment and avoiding undesired consequences - in this case, collision with other ships and powered groundings.
description: Robot Operating System (ROS), 13 (Noetic Ninjemys)
description: Python, 3.8
DOI: 10.18710/hmf7ee/j7kg97
title: 1A_selected-plan.csv
DOI: 10.18710/iuhe48
title: Supplementary Data for: Polymer-based collectors in flotation: A review

description: The dataset includes search results from various databases—Scopus, Web of Science, Scilit, Springer, and ACS Publications—using the keywords 'Polymer', 'Collector', and 'Flotation'. The dataset presents the number of publications per year across various databases, illustrating the growing interest in research on polymeric collectors in flotation. Each publication in the dataset includes detailed information in rows such as authors, title of the publication, differentiation indicating if the polymer was used as a collector or another flotation reagent, DOI (Digital Object Identifier), name of the database, and keywords used for the search.
DOI: 10.18710/l0vpw7/3evxon
title: SBL099_Subt2__3__10-26-06-894.txt
DOI: 10.18710/l0vpw7/b2sof6
title: SBL272_A_Subt2__3__21-19-11-647.txt
DOI: 10.18710/l0vpw7/brxnph
title: SBL068B_Subt4__9__14-37-01-890.txt
DOI: 10.18710/l0vpw7/brxujc
title: SBL043_Subt4__9__15-21-33-274.txt
DOI: 10.18710/l0vpw7/fuz609
title: SBL062_Subt2__11__11-50-33-682.txt
DOI: 10.18710/l0vpw7/rqry19
title: SBL265_A_Subt2__8__15-53-07-380.txt
DOI: 10.18710/l0vpw7/vvyy1t
title: SBL022_Subt4__52__13-47-09-386.txt
DOI: 10.18710/l0vpw7/yjf6oe
title: SBL148_B_Subt8__3__17-42-04-494.txt
DOI: 10.18710/lfhfjn
title: NeRF-4Scenes: A Video Dataset for Subjective Assessment of NeRF

description: The dataset contains 36 NeRF-generated videos captured from four different indoor and outdoor environments: S1 for outdoor, S2 for auditorium, S3 for classroom, and S4 for lounge entrance. Each scene is trained using three NeRF models: Nerfacto as M1, Instant-NGP as M2, and Volinga as M3. Finally, each trained scene is
rendered on three customized trajectories referred to as P1, P2, and P3.

There are a total of 36 videos (4 scenes × 3 models × 3 paths) each having its own individual name. For example, video S1M1P1 corresponds to the outdoor scene (S1), which is trained on the Nerfacto model (M1), and rendered on the first camera path (P1).
description: Nerfstudio API, 1.1.0
DOI: 10.18710/mtuavq/drznza
title: 20230906_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/hs9rvd
title: 20230820_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/i0accc
title: 20230812_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/sev0nc
title: 20230705_BJN_REDTEC_glo.txt
DOI: 10.18710/tgrolw/lytmry
title: 20230205_NYA_REDTEC_gps.txt
DOI: 10.18710/vm0wkq/oxyfwb
title: Chromosome 9.csv

description: Contents: Gene (name of gene), 
 Gene ID (Ensemble ID of gene)
 Causal Estimate Least Squares (estimate of the causal effect of the gene on CAD using the least squares estimator), 
 Causal Estimate GMM (estimate of the causal effect of the gene on CAD using the Generalized method of moments estimator),
 se Least Squares/GMM (standard error of the least squares estimator and the GMM estimator, because the functional form is same, the standard error is same for both estimators),
 p-value GMM (p-value of the GMM estimator), 
 p-value Least Squares (p-value of the least squares estimator),
 Causal Estimate MVIVW (estimate of the causal effect of the gene on CAD using multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 se MVIVW (standard error of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package),
 p-value MVIVW (p-value of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 Causal effect MR (causal effect using univariate Mendelian Randomization), 
 F_stat (conditional F-statistic calculated using the Mendelian Randomization package), 
 Q_stat (cochran's Q-statistic calculated using the Mendelian Randomization package), 
 Q_pval (p-value of the cochran's Q-statistic calculated using the Mendelian Randomization package), 
 lead SNP (the SNP with the lowest p-value in the GWAS summary data for the given locus, hence the most significant SNP), 
 position (position in base pairs of the lead SNP), 
 chromosome (chromosome of the lead SNP), 
 tissue (name of the tissue from where the expression levels of genes are considered in the MVMR causal analysis), 
 No of SNPs (number of SNPs in the MVMR causal analysis), 
 study (GWAS study ebi-a-GCST003116), 
 determinant of LD matrix (the determinant of the Linkage disequilibrium matrix of the SNPs considered in the MVMR causal analysis), 
 determinant of instruments strength matrix (the determinant of the covariance matrix of the instruments and the genes for the SNPs considered in the MVMR causal analysis), 
 LD threshold (maximum allowed LD between SNPs considered as instruments for the causal MVMR analysis).
 
 Purpose: Collection of all results in the MVMR analysis
DOI: 10.18710/l0vpw7/2n5fgh
title: SBL258_B_Subt2__16__21-00-46-852.txt
DOI: 10.18710/l0vpw7/ouelnd
title: SBL119_Subt2__8__18-29-54-793.txt
DOI: 10.18710/l0vpw7/ruuvtx
title: SBL109_Subt2__9__18-19-14-787.txt
DOI: 10.18710/m0osle/0elils
title: 20240506_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/1euuu6
title: 20240615_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/204uxm
title: 20240929_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/9wpv49
title: 20240430_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/b9ross
title: 20240601_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/bs9ovx
title: 20240720_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/e8xybu
title: 20240922_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/g2cdao
title: 20240825_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/i8lxbx
title: 20240626_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/sgxj8e
title: 20240913_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/sjgosw
title: 20240402_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/v8svfl
title: 20240705_KHO_REDOBS_glo.txt
DOI: 10.18710/mtuavq/0d16ux
title: 20230709_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/2j50gc
title: 20230722_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/2z5nn8
title: 20231031_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/6yvzdq
title: 20230807_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/an5qsd
title: 20231117_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/bs72ll
title: 20230809_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/jcrjyz
title: 20230928_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/sz6p20
title: 20230725_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/te51q8
title: 20230808_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/tnq8ws
title: 20230915_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/wg0f09
title: 20231215_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/wuxk7s
title: 20230819_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/znbcrh
title: 20230716_BJN_REDTEC_glo.txt
DOI: 10.18710/tgrolw/c2jsmv
title: 20230119_NYA_REDTEC_gps.txt
DOI: 10.18710/umdoq0/gndgzr
title: 20230120_KHO_REDTEC_gal.txt
DOI: 10.18710/kbeqt9/ldlm5y
title: DV_Data_SKC.zip
DOI: 10.18710/l0vpw7/4v3mns
title: SBL188_Subt2__30__14-30-39-475.txt
DOI: 10.18710/l0vpw7/pt7o3k
title: SBL107_Subt2__21__18-17-09-787.txt
DOI: 10.18710/mtuavq/jx3g87
title: 20231126_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/jzvosp
title: 20231006_BJN_REDTEC_gps.txt
DOI: 10.18710/tgrolw/dwrvai
title: 20230226_NYA_REDTEC_gps.txt
DOI: 10.18710/l0vpw7/gohprd
title: SBL240_A_Subt2__32__15-33-20-373.txt
DOI: 10.18710/l0vpw7/wxv2hp
title: SBL247_A_Subt2__9__16-16-36-389.txt
DOI: 10.18710/mtuavq/ea1pcd
title: 20231115_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/feqgq7
title: 20231109_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/mflegi
title: 20231110_BJN_REDTEC_gal.txt
DOI: 10.18710/tgrolw/fv2piu
title: 20230211_NYA_REDTEC_gps.txt
DOI: 10.18710/tynihf/k7aygb
title: 20230317_HOP_REDTEC_gps.txt
DOI: 10.18710/tynihf/noge0i
title: 20230105_HOP_REDTEC_glo.txt
DOI: 10.18710/tynihf/qg0sfb
title: 20230206_HOP_REDTEC_gal.txt
DOI: 10.18710/umdoq0/8n1k06
title: 20231007_KHO_REDTEC_gal.txt
DOI: 10.18710/umdoq0/tqeces
title: 20231024_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/wtj3yr
title: 20230314_KHO_REDTEC_gal.txt
DOI: 10.18710/l0vpw7/dxdt2y
title: sbc030_Subt4__21__19-31-04-773.txt
DOI: 10.18710/l0vpw7/g29zls
title: SBL183_A_Subt2__25__13-36-29-507.txt
DOI: 10.18710/m0osle/1b0fq4
title: 20240906_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/246pdi
title: 20240727_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/4fayeq
title: 20240821_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/4pxtl9
title: 20240801_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/56whsm
title: 20240819_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/8v8lml
title: 20240715_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/bdgvp0
title: 20240707_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/gwtysd
title: 20240804_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/iwcsfw
title: 20240717_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/m9ohuq
title: 20240826_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/mxdw4j
title: 20240903_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/n33xwc
title: 20240730_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/p30efl
title: 20240813_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/rdhusj
title: 20240729_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/srntb1
title: 20241220_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/xwbtb3
title: 20240708_KHO_REDOBS_glo.txt
DOI: 10.18710/m82d10/parjul
title: 1_body_mass.txt
DOI: 10.18710/mtuavq/cfuudm
title: 20230713_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/dkg8zh
title: 20230923_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/rvyk8z
title: 20231217_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/tbvvql
title: 20231025_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/tfpazu
title: 20230919_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/zxy8fb
title: 20230701_BJN_REDTEC_gal.txt
DOI: 10.18710/tgrolw/kvj8jl
title: 20230103_NYA_REDTEC_gal.txt
DOI: 10.18710/tynihf/gdonzh
title: 20230210_HOP_REDTEC_gps.txt
DOI: 10.18710/tynihf/nkgadj
title: 20230124_HOP_REDTEC_gal.txt
DOI: 10.18710/m0osle/um8djt
title: 20240813_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/xt6es6
title: 20241229_KHO_REDOBS_glo.txt
DOI: 10.18710/mtuavq/3jos3c
title: 20230827_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/aezu6g
title: 20230710_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/bj9jz7
title: 20230809_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/mof8vh
title: 20230825_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/ohvbtr
title: 20230907_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/swzgqw
title: 20230727_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/xce1nm
title: 20230826_BJN_REDTEC_gps.txt
DOI: 10.18710/tgrolw/kn3q11
title: 20230108_NYA_REDTEC_gps.txt
DOI: 10.18710/tgrolw/uoxphd
title: 20230212_NYA_REDTEC_gal.txt
DOI: 10.18710/6owktc/7ro3vm
title: 00_README_L50319_KERGUELEN.txt
DOI: 10.18710/bzog3s/dgyejs
title: scHSC_1_S2_2.fq.gz
DOI: 10.18710/hmf7ee/0vwasx
title: 2SN-B_selected-plan.csv
DOI: 10.18710/hmf7ee/b2zr2q
title: 1A_ASV-data.csv
DOI: 10.18710/hmf7ee/iaoyuy
title: 2SN-A_explored-states.csv
DOI: 10.18710/hmf7ee/qzsuux
title: 2NS-B-C_selected-plan.csv
DOI: 10.18710/hmf7ee/t2md9u
title: 1A-C_scenarios-found.csv
DOI: 10.18710/hmf7ee/weyj79
title: 1B_explored-states.csv
DOI: 10.18710/l0vpw7/67dr5f
title: SBL081_Subt4__13__17-59-36-684.txt
DOI: 10.18710/l0vpw7/poz05q
title: SBL086 _Subt2__2__10-00-06-399.txt
DOI: 10.18710/l0vpw7/xhgmk6
title: SBL080_Subt4__26__17-57-46-682.txt
DOI: 10.18710/m0osle/oezrgt
title: 20240916_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/qit1sz
title: 20240927_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/qrz8mz
title: 20241001_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/y36utg
title: 20240903_KHO_REDOBS_gal.txt
DOI: 10.18710/mtuavq/9vit8q
title: 20230905_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/be4efo
title: 20230912_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/h3ykcu
title: 20230927_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/pavvfx
title: 20230822_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/sjwjgj
title: 20231031_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/zxwvty
title: 20230706_BJN_REDTEC_glo.txt
DOI: 10.18710/umdoq0/dga8pi
title: 20230212_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/faofyf
title: 20230205_KHO_REDTEC_gal.txt
DOI: 10.18710/umdoq0/fx5wov
title: 20230223_KHO_REDTEC_gal.txt
DOI: 10.18710/umdoq0/he48om
title: 20230330_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/ovhwer
title: 20230328_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/um9axd
title: 20230310_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/xlgt01
title: 20230216_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/zznbrc
title: 20230101_KHO_REDTEC_gps.txt
DOI: 10.5073/20240125-113803-0
title: Dataset: Tabellenanhang Handbuch N-Gesamtbilanz Bilanzjahr 2019, Handbuch, 2. überarbeitete Fassung

description: Der vorliegende Datensatz beinhaltet Werte für alle Bilanzglieder der Nationalen Stickstoff-Gesamtbilanz von 1990 bis 2019. Der Datensatz ist komplementär zum Handbuch zur Berechnung der Nationalen Stickstoff-Bilanz (Müller et al., 2024, Berechnung der Stickstoff-Bilanz für die Landwirtschaft in Deutschland - Jahre 1990 bis 2019, 2. Fassung, in: Berichte aus dem Julius Kühn-Institut, Julius Kühn-Institut, Bundesforschungsinstitut für Kulturpflanzen, Quedlinburg, Deutschland.). Die zugeführten und abgeführten Stickstoffmengen in den einzelnen Positionen werden gegeneinander verrechnet und auf die landwirtschaftlich genutzte Fläche Deutschlands bezogen. Somit ergibt sich ein Stickstoff-Saldo je Hektar für Deutschland im entsprechenden Bilanzjahr.
description: The presented dataset comprises values for all items which make up the National-Nitrogen-Balance from 1990 to 2019. The dataset is complementary to the handbook for the calculation of the National-Nitrogen-Balance (Müller et al., 2024, Berechnung der Stickstoff-Bilanz für die Landwirtschaft in Deutschland - Jahre 1990 bis 2019, 2. Fassung, in: Berichte aus dem Julius Kühn-Institut, Julius Kühn-Institut, Bundesforschungsinstitut für Kulturpflanzen, Quedlinburg, Germany.). Nitrogen inputs and outputs are set off against each other and divided by the area used for agriculture in Germany resulting in a Nitrogen-balance per hectare.
DOI: 10.5073/20240408-104443-0
title: Data set: Annually mulched wild flower strips increase the observed wild bee (Apiformes) species richness and abundance in vineyard fallows in the short term

description: The present dataset contains the complete list of bee individuals sampled for a study that investigated the effects of different habitat management treatments in vineyard fallows on bee communities. Bees were sampled in 10 vineyard fallows in the Moselle region (Rhineland-Palatinate, SW Germany), which were partially subjected to different treatments (mulching, sowing of wild flower strips, combination, and untreated control). In total, 9323 individuals from 140 bee species were sampled with hand netting and pan traps in 2013 and 2014. The list of bee individuals contains information on sampling details (site, treatment variant, date, time, method) as well as information on the collected bee individuals (operational taxon, determined genus and species based on morphological identification, sex). An additional data sheet contains information on the location of the study sites. This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany, granted by the Federal Office for Agriculture and Food (BLE; grant number 2811HS003).
DOI: 10.15468/dl.vjxny8
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dichogaster gracilis",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054616-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/6owktc/vd69br
title: CT_L50319_KERGUELEN.csv
DOI: 10.18710/8twps8
title: Replication Data for: Connecting the dots: Relationship between heart rate and Overall Dynamic Body Acceleration (ODBA) in free-ranging cattle

description: &lt;p&gt; This dataset contains all the raw data sets, processing code, and analysis for reproducing and replicating the analysis for the article: Connecting the dots: Relationship between heart rate and Overall Dynamic Body Acceleration (ODBA) in free-ranging cattle.

In total, there are 4 files included: the raw dataset, the averaged dataset, and the R script used for the analysis. The raw data contains heart rate and accelerometry data collected from free-ranging cattle during the summer grazing season. The averaged dataset includes processed data summarizing heart rate and ODBA across 1 minute. The R script ('Analysis.R') performs all the steps of preprocessing, and analysis, producing the results as discussed in the article. &lt;/p&gt;
description: &lt;p&gt; Abstract:&lt;/p&gt; 

&lt;p&gt; Monitoring physiological indicators including heart rate (HR) is crucial for managing animal welfare across diverse settings, from precision livestock farming to wildlife conservation. HR is a reliable indicator of energy expenditure and stress, yet the invasive nature of HR loggers limits their application in wild and free-ranging species. This study explores whether overall dynamic body acceleration (ODBA), measured with an external accelerometer, can serve as a less invasive proxy for HR. Using free-ranging cattle as a model species in Norway, we examined the relationship between ODBA and HR to assess how external accelerometry might indirectly reflect physiological states in settings that resemble wild conditions. Cattle provide an ideal model because they share some characteristics with wild herbivores, including exposure to diverse terrain and potential predation, while offering advantages for handling and sensor retrieval. &lt;/p&gt; 

&lt;p&gt; Our findings showed that low ODBA values corresponded to static behaviours (e.g., standing, ruminating), where small movements caused HR spikes, while higher ODBA reflected dynamic activities (e.g., walking, foraging), with HR plateauing. This relationship suggests that ODBA can be used to approximate HR in environments where direct HR measurement is challenging. By using accelerometry to infer HR changes in free-ranging cattle, this study offers insights that could extend to wild species, offering a tool for conservationists to monitor and manage animal health and wellbeing less invasively. &lt;/p&gt;
description: R, 4.3.3
DOI: 10.18710/l0vpw7/goqpfu
title: SBL258_A_Subt2__10__20-58-50-852.txt
DOI: 10.18710/l0vpw7/ubnbpb
title: SBL271_A_Subt2__5__21-14-19-645.txt
DOI: 10.18710/m0osle/210t3e
title: 20240912_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/5x779u
title: 20240913_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/k4547w
title: 20240730_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/vzdrey
title: 20240901_KHO_REDOBS_gal.txt
DOI: 10.18710/m0osle/wzz1gn
title: 20240803_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/z9thrf
title: 20240901_KHO_REDOBS_glo.txt
DOI: 10.18710/tw9iqd
title: Data for: Stimulus Selection Based on Gender Ratios: Gender Ratios are Indicative of Both Stereotypical and Conceptual Gender

description: This dataset included one exploratory experiment that used an attribute rating task to identify the level to which individuals felt differing attributes were appropriate for female-dominated, male-dominated, and gender-balanced roles. Data was gathered in late 2018 via an anonymous self-administered questionnaire. Each participant responded to all attributes paired with one female-dominated, one male-dominated, and one gender-balanced role. The roles were randomly selected per participant. The order of the roles was randomised per participant, and the order of the attributes was randomised by participant and role. This builds on an earlier attribute naming experiment; due to ethical reasons, the raw data from this first experiment are not included in this dataset. This dataset contains four files; an RStudio code that allows for the straightforward replication of the conducted analyses, a .txt file also containing this code, a .txt file containing the raw data, and a readme.txt file.
DOI: 10.18710/umdoq0/2qr0qp
title: 20230127_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/3jp7ih
title: 20230301_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/6d2oxq
title: 20230223_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/6ewprj
title: 20230209_KHO_REDTEC_gal.txt
DOI: 10.18710/umdoq0/7lcvmw
title: 20230104_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/gf4vgl
title: 20230202_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/i5fv02
title: 20230116_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/ovtzdx
title: 20230302_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/qii2nu
title: 20230201_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/st7rkq
title: 20230126_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/txfmko
title: 20230313_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/xyl3t0
title: 20230318_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/ydqqhh
title: 20230121_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/zsqard
title: 20230207_KHO_REDTEC_gps.txt
DOI: 10.18710/bzog3s/faann1
title: scHSC_3_ST3_1.fq.gz
DOI: 10.18710/bzog3s/pujgag
title: scHSC_4_C2_1.fq.gz
DOI: 10.18710/bzog3s/xoqpgr
title: scHSC_2_T3_1.fq.gz
DOI: 10.18710/hjssok/ddcde5
title: 00_Readme.txt

description: Readme file
DOI: 10.18710/hmf7ee/gdfgm6
title: 2SN-A_TS-data.csv
DOI: 10.18710/hmf7ee/ioxh7j
title: 2SN-B-C_selected-plan.csv
DOI: 10.18710/m0osle/2nzgbl
title: 20240706_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/bvwukj
title: 20240801_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/gapknb
title: 20240930_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/lbfy3m
title: 20240917_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/ld80o6
title: 20240731_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/ndvcld
title: 20240821_KHO_REDOBS_gps.txt
DOI: 10.18710/m0osle/njuvpg
title: 20240822_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/qhiz3t
title: 20240806_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/sugu4u
title: 20240712_KHO_REDOBS_glo.txt
DOI: 10.18710/m0osle/wx06rk
title: 20240814_KHO_REDOBS_glo.txt
DOI: 10.18710/mtuavq/7i5oip
title: 20231020_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/bxpj2v
title: 20231124_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/hp69t1
title: 20231014_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/jwsjk9
title: 20231112_BJN_REDTEC_gal.txt