cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7733315
title: Table_A2-5-9_Ecotoxicological threshold values non-target Arthropods (NTAs)

description: Ecotoxicological threshold values non-target Arthropods (NTAs)
DOI: 10.6084/m9.figshare.22269685
title: This data is linked to "A new strategy for cellulases application in high temperature industrial scenarios with syringic acid assisting"

description: This shared data is used for the publication of articles with the title "<strong>A new strategy for cellulases application in high temperature industrial scenarios with syringic acid assisting"</strong>
DOI: 10.6084/m9.figshare.22269685.v1
title: This data is linked to "A new strategy for cellulases application in high temperature industrial scenarios with syringic acid assisting"

description: This shared data is used for the publication of articles with the title "<strong>A new strategy for cellulases application in high temperature industrial scenarios with syringic acid assisting"</strong>
DOI: 10.5519/qd.erazak9z
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (128 records)
DOI: 10.6084/m9.figshare.22269973.v1
title: 6_Assigning_Putative_Gene_Function_input_files

description: UniProt/SwissProt multi-FASTA file from <em>http://www.uniprot.org</em> used with accompanying scripts.
DOI: 10.6084/m9.figshare.22263910.v1
title: 5_MAKER_Round_2_input_files

description: Parameters for predicting genes with a local AUGUSTUS installation.<strong> Place the species parameter folder into $AUGUSTUS_CONFIG_PATH species folder. </strong>
DOI: 10.6084/m9.figshare.22269973
title: 6_Assigning_Putative_Gene_Function_input_files

description: UniProt/SwissProt multi-FASTA file from <em>http://www.uniprot.org</em> used with accompanying scripts.
DOI: 10.6084/m9.figshare.22263796
title: 4_Training_Gene_Finders_input_files

description: Input files for training SNAP and WebAugustus.
DOI: 10.6084/m9.figshare.22263910
title: 5_MAKER_Round_2_input_files

description: Parameters for predicting genes with a local AUGUSTUS installation.<strong> Place the species parameter folder into $AUGUSTUS_CONFIG_PATH species folder. </strong>
DOI: 10.6084/m9.figshare.22263136
title: 3_MAKER_Round_1_input_files

description: Data files of basic repeat library used by MAKER to mask DNA repeats.
DOI: 10.6084/m9.figshare.22263136.v1
title: 3_MAKER_Round_1_input_files

description: Data files of basic repeat library used by MAKER to mask DNA repeats.
DOI: 10.6084/m9.figshare.22263796.v1
title: 4_Training_Gene_Finders_input_files

description: Input files for training SNAP and WebAugustus.
DOI: 10.6084/m9.figshare.22263040
title: 1_L_giganteum-specific_scaffold_search_input_files

description: Input files to identify and filter NSDO01 contigs with extensive matches to confirmed <em>L. giganteum</em> genes. Used with accompanying scripts.
DOI: 10.6084/m9.figshare.22263040.v1
title: 1_L_giganteum-specific_scaffold_search_input_files

description: Input files to identify and filter NSDO01 contigs with extensive matches to confirmed <em>L. giganteum</em> genes. Used with accompanying scripts.
DOI: 10.6084/m9.figshare.22270087
title: Supplementary Material: Climate Change Influences Brain Size in Humans

description: Abstract Brain size evolution in hominins constitutes a crucial evolutionary trend, yet the underlying mechanisms behind those changes are not well understood. Here, climate change is considered as an environmental factor using multiple paleoclimate records testing temperature, humidity, and precipitation against changes to brain size in 298 <em>Homo</em> specimens over the past fifty thousand years. Across regional and global paleoclimate records, brain size in <em>Homo</em> averaged significantly lower during periods of climate warming as compared to cooler periods. Geological epochs displayed similar patterns, with Holocene warming periods comprising significantly smaller brained individuals as compared to those living during glacial periods at the end of the Late Pleistocene. Testing spatiotemporal patterns, the adaptive response appears to have started roughly fifteen thousand years ago and may persist into modern times. To a smaller degree, humidity and precipitation levels were also predictive of brain size, with arid periods associated with greater brain size in <em>Homo</em>. The findings suggest an adaptive response to climate change in human brain size that is driven by natural selection in response to environmental stress. The Author(s). Published by S. Karger AG, Basel
DOI: 10.5281/zenodo.6561382
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PMT
DOI: 10.5281/zenodo.6561381
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PMT
DOI: 10.5281/zenodo.6577005
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PM
DOI: 10.5281/zenodo.6577004
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PM
DOI: 10.5281/zenodo.6581962
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PMT
DOI: 10.5281/zenodo.6581961
title: A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3

description: Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PMT
DOI: 10.5281/zenodo.7733304
title: Density-wave ordering in a unitary Fermi gas with photon-mediated interactions

description: Data corresponding to the article 'Density-wave ordering in a unitary Fermi gas with photon-mediated interactions'.
DOI: 10.5281/zenodo.7733303
title: Density-wave ordering in a unitary Fermi gas with photon-mediated interactions

description: Data corresponding to the article 'Density-wave ordering in a unitary Fermi gas with photon-mediated interactions'.
DOI: 10.5281/zenodo.7728947
title: Hydrothermal Synthesis of Ni3TeO6 and Cu3TeO6 Nanostructures for Magnetic and Photoconductivity Applications

description: See the reference: Fernández-Catalá, J.; Singh, H.; Wang, S.; Huhtinen, H.; Paturi, P.; Bai, Y.; Cao, W <em>ACS Appl. Nano Mater.</em> <strong>2023 </strong>(https://doi.org/10.1021/acsanm.3c00630)
description: The authors thank: European Union-Next Generation EU, MINECO, and University of Alicante for a postdoctoral research grant (MARSALAS21-09), Jenny and Antti Wihuri foundation, the Center of Materials Analysis (CMA), the Canada Foundation for Innovation (CFI), the Natural Sciences and Engineering Research Council (NSERC), the National Research Council (NRC), the Canadian Institutes of Health Research (CIHR), the Government of Saskatchewan, and the University of Saskatchewan.
DOI: 10.5281/zenodo.7728946
title: Hydrothermal Synthesis of Ni3TeO6 and Cu3TeO6 Nanostructures for Magnetic and Photoconductivity Applications

description: See the reference: Fernández-Catalá, J.; Singh, H.; Wang, S.; Huhtinen, H.; Paturi, P.; Bai, Y.; Cao, W <em>ACS Appl. Nano Mater.</em> <strong>2023 </strong>(https://doi.org/10.1021/acsanm.3c00630)
description: The authors thank: European Union-Next Generation EU, MINECO, and University of Alicante for a postdoctoral research grant (MARSALAS21-09), Jenny and Antti Wihuri foundation, the Center of Materials Analysis (CMA), the Canada Foundation for Innovation (CFI), the Natural Sciences and Engineering Research Council (NSERC), the National Research Council (NRC), the Canadian Institutes of Health Research (CIHR), the Government of Saskatchewan, and the University of Saskatchewan.
DOI: 10.57745/1fg34z
title: Fig1.d_2.DAT

description: Data of figure 1.d, the header describe the column names
DOI: 10.57745/221wzi
title: Fig2.c.dat

description: Data of Fig2.c, the header describe the column name
DOI: 10.57745/aumdfx
title: Fig1.d_1.DAT

description: Data of figure 1.d, the header describe the column names
DOI: 10.57745/orae3o
title: Fig1.c.dat

description: Data of figure 1.c, the header describe the column names
DOI: 10.57745/2u1h3d
title: Fig4.c4.dat

description: Data of Fig.4.c, curve 4. The header describe de column names
DOI: 10.57745/39zj7s
title: Fig2.b.dat

description: Data of Fig2.b, the header describe the column name
DOI: 10.57745/dkph97
title: Fig.3.a-b2.dat

description: Data of Fig.3.b, the header describe the column names
DOI: 10.57745/fr4bzo
title: Fig.3.a-b1.dat

description: Data of Fig.3.a, the header describe the column names
DOI: 10.57745/pdbmyt
title: Fig4.c3.dat

description: Data of Fig.4.c, curve 3. The header describe de column names
DOI: 10.57745/xp9zmf
title: Fig2.a.dat

description: Data of Fig2.a, the header describe the column name
DOI: 10.57745/0blzmx
title: Fig4.b.dat

description: Data of Fig.4.b. The header describe de column names
DOI: 10.57745/ksk1rr
title: Fig.5.a.dat

description: Data of Fig.5.a. The header describe de column names
DOI: 10.57745/nzleya
title: Fig4.c2.dat

description: Data of Fig.4.c, curve 2. The header describe de column names
DOI: 10.57745/r8kttt
title: Fig4.c1.dat

description: Data of Fig.4.c, curve 1. The header describe de column names
DOI: 10.57745/u7dcnv
title: ReadMe.txt

description: Description of data files and figures
DOI: 10.57745/yc1ssk
title: Fig4.a.dat

description: Data of Fig.4.a. The header describe de column names
DOI: 10.57745/cugfxr
title: ReadMe.txt

description: Description of the files and data related to the figures
DOI: 10.57745/htjvug
title: Fig5b.dat

description: Data related to Fig. 5b
DOI: 10.25829/bwh0-9w18
title: Aboveground biomass, CWMs of height, SLA, leaf nutrients, AMF colonization, root traits, soil pH and concentrations of soil C, N, P, K in 85 plots of the Dominance Experiment (1, 2, 6, 9 species) in 2017

description: It is not well understood whether and how plant diversity-dependent soil changes and associated changes in functional traits contribute to positive diversity–productivity relationships in the long run. To test this, we investigated 85 plant communities (1, 2, 6, and 9 species plots) of the Dominance Experiment. In May/June 2017, we measured community biomass production (aboveground biomass), community-means of plant functional traits (plant height, specific leaf area, leaf N, P, K concentrations, specific root length, root length density, AMF colonization rates), and soil properties (soil pH, soil organic C, soil N, P, K concentrations). We tested how these variables changed along the plant diversity gradient, were related to each other, and how plant and soil variables influenced community biomass production.
DOI: 10.6084/m9.figshare.22227451
title: Supplementary Material for: Evaluating a Comprehensive Model of Euthymia

description: Introduction: In research and treatment of mood disorders, “euthymia” traditionally denotes the absence of clinically significant mood disturbance. A newer, expanded definition of euthymia also includes positive affect and psychological well-being. Objective: We aimed to test this comprehensive model of euthymia and estimate the coherence and predictive power of each factor in the model. Methods: Community-dwelling adults (N = 601), including both mental health outpatients and non‑patients at high risk for personality pathology, completed a battery of interviews and questionnaires at time 1. Most (n = 497) were reassessed on average 8 months later (time 2). We modeled euthymia using standard mood, personality, and psychosocial functioning assessments rather than measures designed specifically for euthymia. Results: The hypothesized model of euthymia was supported by confirmatory factor analysis: Specific measures loaded on three lower order factors (mood disturbance, positive affect, and psychological well-being) that reflected general euthymia at time 1. Each factor (general euthymia plus lower order factors) demonstrated moderately strong concurrent (time 1) and predictive (time 1 to 2) correlations with outcomes, including employment status, income, mental health treatment consumption, and disability. Compared to positive affect and psychological well-being, mood disturbance had stronger incremental (i.e., non-overlapping) relations with these outcomes. Conclusions: Support for a comprehensive model of euthymia reinforces efforts to improve assessment and treatment of mood and other disorders. Beyond dampening of psychological distress, euthymia-informed treatment goals encompass full recovery, including enjoyment and meaning in life.
DOI: 10.6084/m9.figshare.22270045
title: An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy‑associated psychiatric comorbidities in an electrical post‑status epilepticus model

description: With this exploratory approach, we aimed to identify microRNAs as potential circulatory biomarkers for epilepsy-associated psychiatric comorbidities across different rat models of epilepsy. The identification of distress-associated biomarkers can also contribute to animal welfare assessment. <br> Raw data sets: <br> File: NEW_CARD_A Description: TaqMan ArrayCard microRNA screening. Corresponding to figure 1a. <br> File: NEW_CARD_B Description: TaqMan ArrayCard microRNA screening. Corresponding to figure 1b. <br> File: electrical-SE_behavior-data_Fig2C Description: Raw data for correlation analysis of selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903) in the electrical post-SE model. Corresponding to figure 2c. <br> File: behavior-miRNA-selected_across_models_Fig4C Description: Raw data for correlation analysis of miR-429 with several behavioral and biochemical parameters across models. Corresponding to figure 4c. <br> <br>
DOI: 10.6084/m9.figshare.22270045.v1
title: An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy‑associated psychiatric comorbidities in an electrical post‑status epilepticus model

description: With this exploratory approach, we aimed to identify microRNAs as potential circulatory biomarkers for epilepsy-associated psychiatric comorbidities across different rat models of epilepsy. The identification of distress-associated biomarkers can also contribute to animal welfare assessment. <br> Raw data sets: <br> File: NEW_CARD_A Description: TaqMan ArrayCard microRNA screening. Corresponding to figure 1a. <br> File: NEW_CARD_B Description: TaqMan ArrayCard microRNA screening. Corresponding to figure 1b. <br> File: electrical-SE_behavior-data_Fig2C Description: Raw data for correlation analysis of selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903) in the electrical post-SE model. Corresponding to figure 2c. <br> File: behavior-miRNA-selected_across_models_Fig4C Description: Raw data for correlation analysis of miR-429 with several behavioral and biochemical parameters across models. Corresponding to figure 4c. <br> <br>
DOI: 10.34740/kaggle/dsv/5165116
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7708906
title: Soundscape and small cetacean sounds. Puerto Cisnes May 2021.

description: Audio files recorded in Puyuhuapi sound, close to the city of Puerto Cisnes, Region de Aysen, Chile, from May, 04th 2021 to May, 06th 2021 . The files with more than 300 detected clicks of small NBHF cetaceans have been uploaded. The cetacean are probably Chilean dolphins (Cephalorhynchus eutropia), but possibly also Peale’s dolphins (Lagenorhynchus australis) or Burmeister’s porpoise (Phocoena spinipinnis). Instrument is QHB Recorder from Smiot, Université de Toulon (http://bioacoustics.lis-lab.fr/smiot), set with sample rate of 512 Ksps, sampling depth of 16 bits, and C57 Cetacean Research hydrophone. The whole dataset, too large to be included in Zenodo repository, can be found on the following adress : http://sabiod.lis-lab.fr/pub/CHILI/PUERTO_CISNES_2021/ .
DOI: 10.5281/zenodo.7708905
title: Soundscape and small cetacean sounds. Puerto Cisnes May 2021.

description: Audio files recorded in Puyuhuapi sound, close to the city of Puerto Cisnes, Region de Aysen, Chile, from May, 04th 2021 to May, 06th 2021 . The files with more than 300 detected clicks of small NBHF cetaceans have been uploaded. The cetacean are probably Chilean dolphins (Cephalorhynchus eutropia), but possibly also Peale’s dolphins (Lagenorhynchus australis) or Burmeister’s porpoise (Phocoena spinipinnis). Instrument is QHB Recorder from Smiot, Université de Toulon (http://bioacoustics.lis-lab.fr/smiot), set with sample rate of 512 Ksps, sampling depth of 16 bits, and C57 Cetacean Research hydrophone. The whole dataset, too large to be included in Zenodo repository, can be found on the following adress : http://sabiod.lis-lab.fr/pub/CHILI/PUERTO_CISNES_2021/ .
DOI: 10.57745/2zxqxl
title: Fig2.d.AMR_NiCu_12K.res

description: Data related to figure 2.d
DOI: 10.57745/inoam4
title: Fig.2.d.AMR_NiCu_300K.res

description: Data related to figure 2.d
DOI: 10.57745/lsyqng
title: ReadMe.txt

description: :unav
DOI: 10.57745/rgagaa
title: Fig3.a.and.b.NL_ISHE_150k_NiPd.txt

description: Data related to figure 3.a and b
DOI: 10.57745/wstsqr
title: Fig2.c.and.f.NL_ISHE_300K_NiCu.res

description: Data related to figure 2.c and f
DOI: 10.57745/yjkmus
title: Fig2.c.and.f.NL_ISHE_12K_NiCu.res

description: Data related to figure 2.c and f
DOI: 10.5519/qd.0t9j7hrm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (762 records)
DOI: 10.34740/kaggle/dsv/5165087
title: AdultoMayor_30_fc
DOI: 10.6084/m9.figshare.22269652
title: Neighborhood landscape context shapes local species richness patterns across continents

description: Data and code for analyses
DOI: 10.6084/m9.figshare.22269652.v5
title: Neighborhood landscape context shapes local species richness patterns across continents

description: Data and code for analyses
DOI: 10.6084/m9.figshare.22269652.v4
title: Neighborhood landscape context shapes local species richness patterns across continents

description: Data and code for analyses
DOI: 10.5281/zenodo.7733489
title: The Geysers catalog

description: The Geysers geothermal field earthquake catalog DateTime,Latitude,Longitude,Depth,Magnitude,MagType,NbStations,Gap,Distance,RMS,Source,EventID
DOI: 10.34658/rdb.hjh1tb/pyrnpq
title: fig 7a.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/1gq3bi
title: fig 13e.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/1krxas
title: fig13b_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/5egowx
title: fig 7d.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/ad2wvb
title: fig 14d.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/rwctxn
title: fig 5_mathematica.nb

description: :unav
DOI: 10.5281/zenodo.7733488
title: The Geysers catalog

description: The Geysers geothermal field earthquake catalog DateTime,Latitude,Longitude,Depth,Magnitude,MagType,NbStations,Gap,Distance,RMS,Source,EventID
DOI: 10.34658/rdb.hjh1tb/nva3qc
title: fig 7d.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/pg65y3
title: fig 14e.png

description: :unav
DOI: 10.34658/rdb.hjh1tb/qmkxq1
title: fig5f_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/rj1k9s
title: fig 2b.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/z2niih
title: fig 6a.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/2djk97
title: fig 10f.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/38be2a
title: fig11a_data-1.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/8e5bu6
title: fig 8c.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/llkson
title: fig4data_i1_-0.05A_i2_0.05A.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/lyotli
title: fig8e_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/fzb3hi
title: fig 7a.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/mj5d6b
title: fig 14e.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/uaewlo
title: fig 13a_b_mathematica.nb

description: :unav
DOI: 10.34658/rdb.hjh1tb/w5qvkl
title: fig 10d.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/wm6ima
title: fig 8b.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/dm3udc
title: fig 7c.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/hmevr9
title: fig 14c.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/rydahm
title: fig 5f.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/wv2i89
title: fig8a_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/xkcil8
title: fig 14a.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/ibajxv
title: fig 5b.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/jrmhtm
title: fig7a_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/v5l213
title: fig 2b.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/wvncis
title: fig 5f.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/yakqu7
title: fig 6_mathematica.nb

description: :unav
DOI: 10.5285/84548d27aac94c32bf94c4d9e7fd5323
title: EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080

description: Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. The projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations. This dataset contains average values of indices for the countries of the UK. Dataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC.
DOI: 10.34658/rdb.hjh1tb/4s72o7
title: fig8c_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/lz0iom
title: fig 9c.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/pclt4b
title: fig 14_a_b_mathematica.nb

description: :unav
DOI: 10.34658/rdb.hjh1tb/y8bhwn
title: fig 2a.png

description: :unav
DOI: 10.34658/rdb.hjh1tb/yrzanu
title: fig 1a.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/1xsfy8
title: fig 7b.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/byej03
title: fig 14b.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/dexldj
title: fig 7e.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/nfakol
title: fig14a_data_currents.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/qaprbl
title: fig 10c.svg

description: :unav
DOI: 10.34658/rdb.hjh1tb/yx2khf
title: fig 8c.pdf

description: :unav
DOI: 10.34658/rdb.hjh1tb/2y6qmw
title: fig5d_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/naskck
title: fig8f_data.txt

description: :unav
DOI: 10.34658/rdb.hjh1tb/t9tqxg
title: fig 14a.svg

description: :unav