cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.m5ek76
title: Occurrence Download

description: A dataset containing 164 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Linum elegans Boiss."
 ]
}

 The dataset includes 164 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0237477-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34816/ifao.archives.120981
title: NÉCROPOLE THÉBAINE_nu_2009_05801

description: Référence : nu_2009_05801<br />Site : Dra Abou el-Naga<br />Secteur : Chapelle (TT_15 (Tetiky)); TT_15 (Tetiky)<br />Type : photographie<br />
DOI: 10.34816/ifao.archives.120982
title: NÉCROPOLE THÉBAINE_nu_2009_05800

description: Référence : nu_2009_05800<br />Site : Dra Abou el-Naga<br />Secteur : Chapelle (TT_15 (Tetiky)); TT_15 (Tetiky)<br />Type : photographie<br />
DOI: 10.15468/dl.jd6z7s
title: Occurrence Download

description: A dataset listing the 118104 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence, Machine Observation, Material sample, Specimen)"
 ]
}

 The dataset's 118104 records were derived from 2251 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012534-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/umdoq0/nomru9
title: 20230715_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/vh1xf6
title: 20230520_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/vq31mt
title: 20230605_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/weyn6e
title: 20230428_KHO_REDTEC_gps.txt
DOI: 10.18710/umdoq0/xkccyx
title: 20230908_KHO_REDTEC_glo.txt
DOI: 10.18710/yaynnl/bojfcz
title: other_functions.py

description: Miscellaneous functions
DOI: 10.18710/zapdyr/mwthp5
title: 002_winter-stressors-synthesis-data.csv
DOI: 10.18710/zzteyt/nhv8iy
title: S1_d4_l2_gr1_and_pl_interpreting_graphs.txt
DOI: 10.18710/zzteyt/tie8ka
title: S1_d2_l2_gr2_and_pl_calculate_using_ratio.txt
DOI: 10.18710/cmktqx/ankiac
title: TOF_2577.txt
DOI: 10.18710/cmktqx/jadror
title: TOF_2480.txt
DOI: 10.18710/cmktqx/jm5ffd
title: TOF_2489.txt
DOI: 10.18710/e9ctjs/ldrrwa
title: 20230501_BJN_REDOBS_gps.txt
DOI: 10.18710/umdoq0/lfju7t
title: 20230406_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/ptprph
title: 20230513_KHO_REDTEC_glo.txt
DOI: 10.18710/4wjck5/uiscce
title: regression_coefficient.txt

description: The regression coefficients for the fit of the EOF coefficients (A1-A4).
DOI: 10.18710/anan9e/qykozy
title: diffcorr_dev_func_of_L.nc

description: Deviations between the diffraction corrections for the transmitter's first radial mode as a function of distance, estimating the effect of model simplifications. This is the data used in Fig. 5 in the paper.
DOI: 10.18710/cfbqsn/kcshlg
title: 2022-10-02_ID_1230_1231.pdf
DOI: 10.18710/cmktqx/f57aer
title: TOF_2560.txt
DOI: 10.18710/cmktqx/ixps5a
title: TOF_2616.txt
DOI: 10.18710/cmktqx/mcea7l
title: TOF_2633.txt
DOI: 10.18710/cmktqx/qir4sf
title: TOF_2576.txt
DOI: 10.18710/cmktqx/r5m5p6
title: TOF_2599.txt
DOI: 10.18710/cmktqx/zxczee
title: TOF_2602.txt
DOI: 10.18710/e9ctjs/bayo3y
title: 20230217_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/diqki1
title: 20230107_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/hhbd88
title: 20230210_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/pj2oxs
title: 20230111_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/y5jtik
title: 20230215_BJN_REDOBS_glo.txt
DOI: 10.18710/h8ektm/03dsu3
title: F064-2.001.iey
DOI: 10.18710/h8ektm/26kxqc
title: F063-2.001.ihx
DOI: 10.18710/h8ektm/88kkix
title: F045-2.001.ihy
DOI: 10.18710/h8ektm/boznjw
title: F066-2.001.dey
DOI: 10.18710/h8ektm/bq3gyo
title: Fn56-2.001.ihy
DOI: 10.18710/h8ektm/d6ec0k
title: F066-2.001.dhx
DOI: 10.18710/h8ektm/ehh2nr
title: Fn83-2.001.iex
DOI: 10.18710/h8ektm/hlvkua
title: FSUB-2.001.dhx
DOI: 10.18710/h8ektm/itogsv
title: F045-2.001.dex
DOI: 10.18710/h8ektm/ru7yjx
title: Fn37-2.002.iey
DOI: 10.18710/hnttsn/5epcm4
title: StableOrganizationoftheEarlyLexicalSemanticNetwork_FixationInTime.csv
DOI: 10.18710/hnttsn/j265m8
title: 00_README.txt
DOI: 10.18710/lodkef/migfwo
title: T_sum_4N.csv
DOI: 10.18710/lodkef/v5kidd
title: 00-Read_Me.txt
DOI: 10.18710/t1vqla/4e32gm
title: _Simulation_Log_1_Sheet_1.csv
DOI: 10.18710/t1vqla/yu9qto
title: 4_5_2023_A_Ship_Dynamics.csv
DOI: 10.18710/umdoq0/bd8h6y
title: 20230607_KHO_REDTEC_gal.txt
DOI: 10.18710/umdoq0/bsmjlo
title: 20231102_KHO_REDTEC_glo.txt
DOI: 10.18710/umdoq0/i8zf87
title: 20230724_KHO_REDTEC_gal.txt
DOI: 10.18710/zzteyt/wv11wo
title: 00_README.txt
DOI: 10.18710/3haxnr/ayge6h
title: 3_REF_DUD.xml
DOI: 10.18710/3lt3qu
title: Supporting Data for: Mineral precipitation and geometry alteration in porous structures: How to upscale variations in permeability-porosity relationship?

description: Porous materials in natural and engineered environments are subject to morphological changes resulting from interacting chemical and physical processes. The complexity of coupled flow, transport, and chemical processes that occur on different temporal and spatial scales makes it difficult to predict the resulting porosity and permeability alterations. Delineating the controls of mineral precipitation reactions is particularly challenging because it requires the implementation of nucleation criteria and precipitation attributes. By conducting pore-scale simulations, we studied how the amount and stochastic distribution of crystallites, controlled by nucleation, affect the pore geometry and permeability in two-dimensional porous structures. The observed relationships between porosity and permeability show characteristics that differ from ones that are typically applicable in dissolving porous media because of the clogging effect. Additionally, we propose a stochastic framework that upscales the co-evolution of permeability and porosity across length scales. This framework enables precise communication of clogging behavior to continuum-scale simulations based on statistical probability distributions of permeability-porosity variations.

Keywords: Upscaling; Probabilistic nucleation; Mineral precipitation; Geometry evolution; Permeability-Porosity; Reactive transport; Porous media
description: Python, 3
DOI: 10.18710/4wjck5/k6tuld
title: EOF_base_function_E1.txt

description: the first EOF base function
DOI: 10.18710/5tw8yf/oeoszq
title: HABS 5.csv
DOI: 10.18710/5tw8yf/oihxu2
title: HABS 1.csv
DOI: 10.18710/8f0q0l/ot5z1t
title: Listening_to_Accents_Intelligibility.csv

description: Participants' orthographic transcriptions of the eight speakers of English.
DOI: 10.18710/bqol1b/m74mb2
title: Petrophysical data.xlsx
DOI: 10.18710/ch7jaw/f2xopu
title: waterVaporNaClEOS.py
DOI: 10.18710/ch7jaw/z2zevr
title: LSDALTON.OUT
DOI: 10.18710/cmktqx/mca2gw
title: TOF_2501.txt
DOI: 10.18710/ddgnwa/oigc1l
title: snow_ice_at_deployment_drifter_206794.xlsx
DOI: 10.18710/ddgnwa/pgzpwd
title: overview_map_drift_trajectories.png
DOI: 10.18710/fyfnfv
title: Sources and Targets in Kuteva et al. 2019

description: &lt;p&gt;This dataset is based on examples found in Kuteva et al. 2019:&lt;/p&gt;
&lt;p&gt;Kuteva, Tania, Bernd Heine, Bo Hong, Haiping Long, Heiko Narrog, and Seongha Rhee. 2019. World Lexicon of Gramaticalization (2nd ed.). Cambridge: Cambridge University Press.&lt;/p&gt;
&lt;p&gt;Kuteva et al.’s World Lexicon of Grammaticalization (2019) is an inventory of examples of morphological reanalysis observed across a sample of over 900 languages. The goal of Kuteva et al.’s inventory is to represent grammaticalization changes that are documented in multiple languages. The examples are cataloged as types defined by Source to Target shifts, such as Ablative &gt; Partitive, that are attested in two or more languages of the sample. The inventory lists 526 Source &gt; Target types.&lt;/p&gt;
&lt;p&gt;The purpose of this dataset is to explore how many Sources also serve as Targets, and to provide a broad semantic classification of the Sources. The classification is intended only for general description of patterns, and does not represent a precise assignment to mutually exclusive classes. Its purpose is to give a qualitative overview and thus does not lend itself to further quantitative analysis.&lt;/p&gt;
&lt;p&gt;This dataset is the basis for the analysis in Section 4 of this publication:&lt;/p&gt;
&lt;p&gt;Janda, Laura A. To Appear. “Morphological reanalysis: recycling old form to new function”, as part of Volume 3 Morphology & Syntax, Part 1 Morphology of The Wiley Blackwell Companion to Diachronic Linguistics, edited by Edith Aldridge, Anne Breitbarth, Katalin É. Kiss, Adam Ledgeway, Joe Salmons, and Alexandra Simonenko.&lt;/p&gt;
DOI: 10.18710/h8ektm/4k9hnw
title: 143.cfri
DOI: 10.18710/h8ektm/hvsjml
title: Fn73-2.001.dex
DOI: 10.18710/h8ektm/pgtq7e
title: F105-2.001.dhx
DOI: 10.18710/h8ektm/uzu9vh
title: Fn56-2.002.dex
DOI: 10.18710/lp9ynh/xidyf1
title: Greek_Term_Σαρχ_in_Josephus.pdf
DOI: 10.18710/pgzhzj/jmdumz
title: 02_Carcass_carnivores.csv

description: Data file, csv format
DOI: 10.18710/qxqbkg
title: Replication Data for "The architecture of a root zone of a large magmatic conduit system from high resolution magnetic, gravity and petrophysical data: the Reinfjord Ultramafic Complex"

description: The dataset includes the raw and processed gravity data acquired in 2015 with a LaCoste & Romberg G-569 gravimeter, the input data (land-gravity and high-resolution helicopter magnetic data) for 3D modeling of the Reinfjord ultramafic complex and the derived 3D model.
description: Tensor Research ModelVision software, 17.5
DOI: 10.18710/t1vqla/ijihco
title: _Simulation_Log_1_Sheet_3.csv
DOI: 10.18710/t1vqla/l2xnwc
title: savedrecs_14.txt
DOI: 10.18710/u5rnge/ciz3qq
title: 05_olefins_1.zip
DOI: 10.18710/u5rnge/tuuwli
title: 12_homopropargylic alcohols_8.zip
DOI: 10.18710/zzteyt/pb6tlp
title: S1_d5_l3_pl_group_presentations.txt
DOI: 10.18710/1u2aqj/mantxk
title: ukr-rus-results_sample.csv
DOI: 10.18710/bqol1b/anoewr
title: S7_NRM.txt
DOI: 10.18710/bqol1b/iz1xab
title: S8_NRM_MTJ.txt
DOI: 10.18710/ddgnwa/1xv90s
title: drifter_206812_trajectory.txt
DOI: 10.18710/ddgnwa/iulciv
title: drifter_206759_trajectory.txt
DOI: 10.18710/ddgnwa/o5iy62
title: S1_drifter_206811.txt
DOI: 10.18710/ddgnwa/qw9mia
title: drifter_206770_trajectory.txt
DOI: 10.18710/h8ektm/0bz7ew
title: F105-2.001.dhy
DOI: 10.18710/h8ektm/7jfkpw
title: FSUA-2.002.dey
DOI: 10.18710/h8ektm/mujvk1
title: F074.2023-02-16.edi
DOI: 10.18710/h8ektm/wmyeyn
title: F075-2.001.ihy
DOI: 10.18710/hjssok/2qmyvn
title: Appendix_1.tif

description: The original μ-CT data as TIF-file
DOI: 10.18710/iexvvn/shenza
title: 00_README_perdon_20231221.txt
DOI: 10.18710/zfadjs/vl3hui
title: Electrochemistry.zip
DOI: 10.18710/cfbqsn/alxm3v
title: 2022-10-02_ID_NA.pdf
DOI: 10.18710/ddgnwa/a2cgdt
title: drifter_206755_trajectory.txt
DOI: 10.18710/ddgnwa/fzhwhz
title: snow_ice_at_deployment_drifter_206759.pdf
DOI: 10.18710/ddgnwa/jsdjzi
title: S1_drifter_206810.txt
DOI: 10.18710/ddgnwa/kxag02
title: S1_drifter_206799.txt
DOI: 10.18710/ddgnwa/nh9aiv
title: about_drifter_206811.txt
DOI: 10.18710/ddgnwa/q61h22
title: snow_ice_at_deployment_drifter_206766.pdf
DOI: 10.18710/ddgnwa/rvnmnn
title: snow_ice_at_deployment_drifter_206771.xlsx
DOI: 10.18710/ddgnwa/ur6d20
title: drifter_206797_trajectory.txt
DOI: 10.18710/e9ctjs/667ro8
title: 20230324_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/byoiu9
title: 20230310_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/dromj1
title: 20230314_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/gt1pte
title: 20230307_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/hpq2lj
title: 20230101_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/ristqz
title: 20230105_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/yyvyag
title: 20230112_BJN_REDOBS_gps.txt
DOI: 10.18710/h8ektm/zvi99r
title: Fn84-2.002.dex
DOI: 10.18710/t1vqla/0qhzx3
title: Open sea_COLREG_tutor_scenario_ 2021_09_27_forces_OS_.csv
DOI: 10.18710/t1vqla/akehdg
title: Final_resutl_3_17_May_2023.txt
DOI: 10.18710/t1vqla/lt1yqx
title: WP1_original_zigzag2020.xlsx
DOI: 10.18710/t1vqla/pqaduk
title: WP1_zigzag_1.csv
DOI: 10.18710/t1vqla/q89kss
title: _Simulation_FM_Log1_Sheet_1.csv
DOI: 10.18710/t1vqla/wfgrvq
title: _Simulation_Log_Sheet_12.csv
DOI: 10.18710/t1vqla/xpjj2v
title: 3_5_2023_F.csv
DOI: 10.18710/zfadjs/yfkgta
title: Electrochemistry.zip
DOI: 10.18710/cfbqsn/1hsqqn
title: 2022-10-02_ID_1301.pdf
DOI: 10.18710/ddgnwa/91bpz8
title: snow_ice_at_deployment_drifter_206822.pdf
DOI: 10.18710/ddgnwa/ilu68b
title: snow_ice_at_deployment_drifter_206794.pdf
DOI: 10.18710/e9ctjs/2h9jsj
title: 20230124_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/im4lov
title: 20230221_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/nsgokg
title: 20230318_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/ntdkcq
title: 20230104_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/vsx8hf
title: 20230120_BJN_REDOBS_glo.txt
DOI: 10.18710/ffanm0/cwvuyg
title: PacBio_VSG_filtered_reads_balbc_10_5.fasta
DOI: 10.18710/fvhtfm
title: Background data for: Advancing our understanding of dispersion measures in corpus research

description: &lt;p&gt;&lt;b&gt;Dataset description&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;This dataset contains background data and supplementary material for Sönning (forthcoming), a study that looks at the behavior of dispersion measures when applied to text-level frequency data. For the literature survey reported in that study, which examines how dispersion measures are used in corpus-based work, it includes tabular files listing the 730 research articles that were examined as well as annotations for those studies that measured dispersion in the corpus-linguistic (and lexicographic) sense. As for the corpus data that were used to train the statistical model parameters underlying the simulation study reported in that paper, the dataset contains a term-document matrix for the 49,604 unique word forms (after conversion to lower-case) that occur in the Brown Corpus. Further, R scripts are included that document in detail how the Brown Corpus XML files, which are available from the Natural Language Toolkit (Bird et al. 2009; https://www.nltk.org/), were processed to produce this data arrangement.&lt;/p&gt;
description: &lt;p&gt;&lt;b&gt;Abstract: Related publication&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;This paper offers a survey of recent corpus-based work, which shows that dispersion is typically measured across the text files in a corpus. Systematic insights into the behavior of measures in such distributional settings are currently lacking, however. After a thorough discussion of six prominent indices, we investigate their behavior on relevant frequency distributions, which are designed to mimic actual corpus data. Our evaluation considers different distributional settings, i.e. various combinations of frequency and dispersion values. The primary focus is on the response of measures to relatively high and low sub-frequencies, i.e. texts in which the item or structure of interest is over- or underrepresented (if not absent). We develop a simple method for constructing sensitivity profiles, which allow us to draw instructive comparisons among measures. We observe that these profiles vary considerably across distributional settings. While D and DP appear to show the most balanced response contours, our findings suggest that much work remains to be done to understand the performance of measures on items with normalized frequencies below 100 per million words.&lt;/p&gt;
description: MAXQDA Plus, 22.5.0
description: R, 4.2.1
DOI: 10.18710/qmvict
title: Supporting Data for: Bilayer Membranes with Frequent Flip-Flops Have Tensionless Leaflets
title: MD simulations of GM1-containing asymmetric lipid bilayers in MARTINI 2.2: systems where monolayer were tension-balanced.

description: Dataset contains long MARTINI (v 2.2) simulations of GM1-containing lipid membranes of POPC (POPC = 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine, GM1 = ganglioside GM1 or monosialotetrahexosylganglioside). Only one monolayer contains GM1s and the opposite monolayer contains the amount of POPC, that makes surface tension of both monolayers almost equal (see Fig. 3 in the reference paper).

Every folder contains the archive "misc.zip", which in turn contains everything for reproduction of the simulations. Please use TREE VIEW to browse files efficiently.
description: GROMACS, 5.1.1
description: GROMACS-LS, 4.5.5
DOI: 10.18710/t1vqla/dglore
title: _Simulation_Log_Sheet.csv
DOI: 10.18710/t1vqla/jxvu5b
title: 4_5_2023_F.csv
DOI: 10.18710/t1vqla/olqtfl
title: _Simulation_Log_Sheet_5.csv
DOI: 10.18710/t1vqla/uqrazi
title: WP1_zigzag_4.csv
DOI: 10.18710/1u2aqj/b1fxku
title: rnc-search_13.html.extracted
DOI: 10.18710/1u2aqj/jwfhfc
title: rnc-search_07.html.extracted
DOI: 10.18710/2o7qsp/kibnrq
title: Test_2_raw_I_joist-1.csv
DOI: 10.18710/c1r1in/k3wg3q
title: AKMA1_SST6_Dive26a_orthomosaic.tif
DOI: 10.18710/cfbqsn/4v7a3u
title: questionnaire_NO_AFTER.pdf
DOI: 10.18710/cfbqsn/iuohiy
title: 2022-10-01_ID_1182.pdf
DOI: 10.18710/cfbqsn/tk4epy
title: 2022-09-21_ID_NA_3.pdf
DOI: 10.18710/ddgnwa/agnhsk
title: snow_ice_at_deployment_drifter_206810.pdf
DOI: 10.18710/e9ctjs/4yhkmc
title: 20230324_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/eqhrjz
title: 20230214_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/f2wj76
title: 20230112_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/od8f6k
title: 20230313_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/u1tsx6
title: 20230220_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/unqekc
title: 20230124_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/xoqrqc
title: 20230630_BJN_REDOBS_gal.txt
DOI: 10.18710/h8ektm/f2c2zf
title: F066.2023-02-16.edi
DOI: 10.18710/h8ektm/qstqe8
title: Fn77-2.002.iey
DOI: 10.18710/h8ektm/uols0l
title: mpirun.sh
DOI: 10.18710/h8ektm/zklauz
title: Fn95-2.001.dhx
DOI: 10.18710/t1vqla/m6effs
title: _Simulation_Log_Sheet_8.csv
DOI: 10.18710/vm0wkq/eatfht
title: GTEx_Sklm_6_160424890.csv
DOI: 10.18710/vxgpii/erbq6t
title: with_Aset_5_Foil_data_measured.dat
DOI: 10.18710/zfadjs/3hxnnl
title: Na3Bi.zip
DOI: 10.18710/zfadjs/4nmbzq
title: Python_scripts.zip
DOI: 10.18710/zfadjs/l1rhhi
title: XRD_and_XAS_scripts.zip
DOI: 10.18710/zfadjs/soqw8r
title: 0_samples_in_published_figures.pdf

description: Overview of figures in related publications with references to the sample IDs of the samples used in the figures. This file makes it possible to quickly locate the data files used in specific figures.
DOI: 10.18710/zfadjs/vb6dxi
title: MoO3_Sigma-Aldrich.zip
DOI: 10.18710/zfadjs/xoyio3
title: Ex_situ_XAS_Mo_k_edge.zip
DOI: 10.18710/zfadjs/xu7cxk
title: Operando.zip
DOI: 10.18710/zojhgj/hastbh
title: Scripts and function.zip

description: When unzipped contains the Matlab and Python scripts used
DOI: 10.18710/1u2aqj/tezwld
title: rnc-search_07.html.extracted
DOI: 10.18710/5tw8yf/9r3osq
title: HABS 7.csv
DOI: 10.18710/5tw8yf/9wcy7y
title: ultradian-914.csv
DOI: 10.18710/8f0q0l/sp8ag6
title: Listening_to_Accents_Pretest.csv

description: Participants' identification accuracy of the selected accents of English in the pretest.
DOI: 10.18710/cfbqsn/sgbajj
title: questionnaire_results.csv
DOI: 10.18710/cfbqsn/zlxv2n
title: 2022-09-25_ID_333.pdf
DOI: 10.18710/e9ctjs/1ok4mx
title: 20230601_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/2xaoom
title: 20230301_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/apjlp4
title: 20230502_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/atkfzy
title: 20230108_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/i221eb
title: 20230215_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/im4okm
title: 20230317_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/mvowh0
title: 20230227_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/npcbkn
title: 20230118_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/rikmte
title: 20230306_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/tu7ykm
title: 20230217_BJN_REDOBS_glo.txt
DOI: 10.18710/h8ektm/v0zwvz
title: Fn74-2.001.iex
DOI: 10.18710/hqdbyf/ngr4gj
title: 20190717_H9c2-G2_mCh-OMP_inFBS_mC1to2000-10min_1520_convTL_003_ALX_D3D_PRJ.avi
DOI: 10.18710/hqdbyf/on6lcr
title: 20190717_H9c2-G3_eGFP-OMP_LTR50nM_mC1to2000-40min_1520_convtTL_008_ALX_D3D_PRJ.avi
DOI: 10.18710/hqdbyf/vuhoxr
title: 20190717_H9c2-G2_mCh-OMP_inFBS_mC1to2000-10min_1520_convtTL_008_ALX_D3D.dv
DOI: 10.18710/lodkef/2upb7k
title: T_sum_allA.csv
DOI: 10.18710/lodkef/328eqw
title: preprocess_settings.m
DOI: 10.18710/lodkef/iofccn
title: WM_measures.mat
DOI: 10.18710/pgzhzj
title: Replication data for: Carcass provisioning and intra-guild risk avoidance between two sympatric large carnivores

description: The current dataset is the replication data for article titled Carcass provisioning and intra-guild risk avoidance between two sympatric large carnivores written by Nordli (2024), accepted for publication in Behavioral Ecology and Sociobiology. The dataset includes remote wildlife camera data from wolf-killed moose in south eastern Norway in 2014 and 2015.The article (10.1007/s00265-023-03423-6) revolves around the competitive relationship between sympatric wolves (Canis lupus) and wolverines (Gulo gulo) in Norway, particularly focusing on their interactions during scavenging on fresh wolf kills across different seasons. The study utilized remote cameras deployed at 29 fresh wolf kills, and Bayesian generalized linear mixed models (GLMMs) were constructed to analyze the use of these kills by both wolves and wolverines.
DOI: 10.18710/r8xrz4
title: Replication Data for: CO2 stunning in pigs: Physiological deviations at onset of excitatory behaviour.

description: Stunning by carbon dioxide (CO2) inhalation is controversial, and excitatory behaviour is commonly observed. It is still unknown which behaviours indicate transition into unconsciousness. Our study objective was to investigate loss of consciousness by linking physiological variables to the start of behaviours observed during CO2 inhalation. The dataset consist of physiological and behavioural data used in the study.
description: icollect, 5.0
description: ABL 800 Flex, Radiometer
DOI: 10.18710/rdpxql/0yvgtt
title: Figure9.zip
DOI: 10.18710/rdpxql/qw6fyd
title: Figure10.txt
DOI: 10.18710/t1vqla/7xpmot
title: 3_5_2023_F_Ship_Dynamics.csv
DOI: 10.18710/u5rnge/muraa1
title: 07_iodohydrins_3.zip
DOI: 10.18710/11lltw/m63t8f
title: 20210611_H9c2-dTag_CCCP10uM_1518_conTL_003_ALX_PRJ.avi
DOI: 10.18710/11lltw/smqkld
title: 20200227_H9c2-galac-mCh-eGFP_LTDR50nM-30min_1522_dvtl_008_ALX_PRJ.avi
DOI: 10.18710/e9ctjs/0vziic
title: 20230504_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/1otwjq
title: 20230527_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/6anid5
title: 20230114_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/76o14x
title: 20230320_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/86n8qk
title: 20230406_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/8yjtqi
title: 20230525_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/9ge1p5
title: 20230428_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/dnfy9v
title: 20230526_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/mumuvq
title: 20230417_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/pho358
title: 20230511_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/qhfd1q
title: 20230623_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/qmfdb2
title: 20230402_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/rkkktm
title: 20230521_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/sgew9q
title: 20230212_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/t4iee3
title: 20230416_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/txqiz2
title: 20230503_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/we0kpc
title: 20230506_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/ygpf3a
title: 20230410_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/0pcylj
title: 20230404_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/5orbo7
title: 20230401_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/9cembu
title: 20230425_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/fj5pge
title: 20230818_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/gszqpp
title: 20230612_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/jhxiua
title: 20230526_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/nqujh2
title: 20230408_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/ox5voc
title: 20230615_BJN_REDOBS_gps.txt
DOI: 10.18710/5kce4u/ljvy2i
title: data_jenset_mcgillivray_downsampling.tsv

description: Tab-delimited data table containing the 11,645 annotated verb tokens
DOI: 10.18710/bax9n5/vzqmau
title: percent-completed-calculation.sh.zip