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Рис. 2. Основные части теΛа и эΛементы окраски Mesobuthus eupeus: I – просома; II – мезосома; III – метасома; a – меΑиаΛьная поΛоса; b – парамеΑиаΛьная поΛоса; с – маргинаΛьная поΛоса; d – поперечная поΛоса; e – пятна. Fig. 2. Main body parts and colour pattern elements of Mesobuthus eupeus: I – prosoma; II – mesosome; III – metasoma; a – medial stripe; b – paramedical stripe; с – marginal stripe; d – transverse stripe; e – spots. in Materials on the colour pattern variability of Mesobuthus eupeus (C.L. Koch, 1839) (Arachnida: Scorpiones) in southeastern Shirvan and Gobustan (Eastern Azerbaijan)
Рис. 2. Основные части теΛа и эΛементы окраски Mesobuthus eupeus: I – просома; II – мезосома; III – метасома; a – меΑиаΛьная поΛоса; b – парамеΑиаΛьная поΛоса; с – маргинаΛьная поΛоса; d – поперечная поΛоса; e – пятна. Fig. 2. Main body parts and colour pattern elements of Mesobuthus eupeus: I – prosoma; II – mesosome; III – metasoma; a – medial stripe; b – paramedical stripe; с – marginal stripe; d – transverse stripe; e – spots.
Рис. 21–25. Частота изменчивости основных эΛементов рисунка (меΑиаΛьной, парамеΑиаΛьных и маргинаΛьных поΛос) ΑорсаΛьной поверхности мезосомы Mesobuthus eupeus в посеΛениях кажΑой из выборок. 21 – Северо-ЗапаΑный Гобустан (n = 57); 22 – Северо-Восточный Гобустан (n = 40); 23 – ЦентраΛьный Гобустан (n = 81); 24 – Юго-Восточный Гобустан (n = 103); 25 – Юго-Восточный Ширван (n = 86). По оси абсцисс указаны посеΛения, по оси орΑинат – частота изменчивости. Figs 21–25. Frequency of variability of the main elements of pattern (medial, paramedical and marginal stripes) of dorsal surface of mesosome of Mesobuthus eupeus in conglomerations of each sample. 21 – northwestern Gobustan (n = 57); 22 – northeastern Gobustan (n = 40); 23 – central Gobustan (n = 81); 24 – southeastern Gobustan (n = 103); 25 – southeastern Shirvan (n = 86). Abscissas show conglomerations, ordinates – the frequency of variability. in Materials on the colour pattern variability of Mesobuthus eupeus (C.L. Koch, 1839) (Arachnida: Scorpiones) in southeastern Shirvan and Gobustan (Eastern Azerbaijan)
Рис. 21–25. Частота изменчивости основных эΛементов рисунка (меΑиаΛьной, парамеΑиаΛьных и маргинаΛьных поΛос) ΑорсаΛьной поверхности мезосомы Mesobuthus eupeus в посеΛениях кажΑой из выборок. 21 – Северо-ЗапаΑный Гобустан (n = 57); 22 – Северо-Восточный Гобустан (n = 40); 23 – ЦентраΛьный Гобустан (n = 81); 24 – Юго-Восточный Гобустан (n = 103); 25 – Юго-Восточный Ширван (n = 86). По оси абсцисс указаны посеΛения, по оси орΑинат – частота изменчивости. Figs 21–25. Frequency of variability of the main elements of pattern (medial, paramedical and marginal stripes) of dorsal surface of mesosome of Mesobuthus eupeus in conglomerations of each sample. 21 – northwestern Gobustan (n = 57); 22 – northeastern Gobustan (n = 40); 23 – central Gobustan (n = 81); 24 – southeastern Gobustan (n = 103); 25 – southeastern Shirvan (n = 86). Abscissas show conglomerations, ordinates – the frequency of variability.
Repetitive elements of Erebia and Carex and their genomic proportions
<p>Datasets of repetitive elements in the genomes of <em>Erebia </em>and <em>Carex</em>, detected and annotated using RepeatExplorer2 (Novák et al., 2010, 2020) using low-coverage (0.1X) short read sequencing data. Data from the article "Holocentric repeat landscapes: from microevolutionary patterns to macroevolutionary associations with karyotype evolution":</p> <p>Cornet, C., Mora, P., Augustijnen, H., Nguyen, P., Escudero, M., & Lucek, K. (2023). Holocentric repeat landscapes: From micro-evolutionary patterns to macro-evolutionary associations with karyotype evolution. Molecular Ecology, 00, 1–19. <a href="https://doi.org/10.1111/ mec.17100">https://doi.org/10.1111/ mec.17100</a></p> <p>47 <em>Erebia </em>and 14 <em>Carex </em>species were analysed in genus-level analyses ("Erebia" and "Carex" folders).<br> In addition, individuals of 4 <em>Erebia </em>species ("Erebia cassioides", "Erebia tyndarus", "Erebia nivalis" and "Erebia pronoe" folders) from different populations were analysed in species-level analyses. </p> <p>Subfolders "Individuals" and "Comparative" represent the two modes in which RepeatExplorer2 was run: the individual mode identifies repeats in each sample separately, and the comparative mode identifies repeats in all samples simultaneously, allowing comparisons between individuals and species. </p> <p>Files named "CLUSTER_TABLE..." are the raw output of RepeatExplorer2 and represent the overall number of reads in each cluster of repetitive element, and their annotation.<br> Files named "COMPARATIVE_ANALYSIS_COUNTS..." are the raw output of RepeatExplorer2 in comparative mode, representing the number of reads in each cluster for each sample included in the analysis. <br> Files named "Genome_proportion..." are the genomic proportion of each repeat annotation, calculated as the proportion of reads with the same annotation.</p> <p>Refer to Cornet et al. (2023) in Molecular Ecology for more details on how the data was generated, the downstream analyses and the sample names (see Tables S1, S2 and S3). </p> <p>References:</p> <p>Novák, P., Neumann, P., & Macas, J. (2010). Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data. BMC Bioinformatics, 11(1), 378. https://doi.org/10.1186/1471-2105-11-378</p> <p>Novák, P., Neumann, P., & Macas, J. (2020). Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nature Protocols, 15(11), Article 11. https://doi.org/10.1038/s41596-020-0400-y</p>
IODP Expedition 382 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
IODP Expedition 382 ICP-AES elemental analysis (solids)
Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 382 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 392 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 392 ICP-AES elemental analysis (solids)
Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
IODP Expedition 392 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
Finite Element Analysis perturbation files for Rubin Observatory Simonyi Survey Telescope and LSST Camera
<p>## Notes on FEA files</p> <p><br> </p> <p># M1M3 Bending modes</p> <p> </p> <p>M1M3_1um_156_grid.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_grid.txt</p> <p>- shape = (5256, 159)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- 0th column is M1M3 disambiguator</p> <p>- 1st and 2nd columns are FEA node x and y in M1M3 CS</p> <p>- Last 156 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M1M3_1um_156_force.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_force.txt</p> <p>- shape = (156, 159)</p> <p>- Each row is one of 156 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M1M3 CS</p> <p>- Last 156 columns are forces in Newtons for each mode.</p> <p><br> </p> <p># M1M3 print through</p> <p> </p> <p>M1M3_dxdydz_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_zenith.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is zenith pointing</p> <p> </p> <p>M1M3_dxdydz_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_horizon.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is horizon pointing</p> <p> </p> <p>M1M3_force_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_zenith.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is zenith pointing. (Is this after optimization? Include LUT or not?)</p> <p> </p> <p>M1M3_force_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_horizon.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is horizon pointing. (Is this after optimization? Include LUT or not?)</p> <p><br> </p> <p># M1M3 Thermal</p> <p> </p> <p>M1M3_thermal_FEA.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_thermal_FEA.npy</p> <p>- shape = (5244, 7)</p> <p>- Each row is one of 5244 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Bulk temperature dz coefficient</p> <p>- 3: x temperature gradient dz coefficient</p> <p>- 3: y temperature gradient dz coefficient</p> <p>- 3: z temperature gradient dz coefficient</p> <p>- 3: r temperature gradient dz coefficient</p> <p><br> </p> <p># M1M3 Miscellany</p> <p> </p> <p>M1M3_influence_256.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_influence_256.npy</p> <p>- shape = (5256, 256)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Each column is one of 256 actuators.</p> <p>- Values are dz/dF for each actuator/node.</p> <p> </p> <p>M1M3_LUT.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_LUT.txt</p> <p>- shape = (257, 91)</p> <p>- First column is index in degrees (0-90 inclusive). Last 256 columns are forces in Newtons.</p> <p>- Each column is LUT for one value of the elevation index.</p> <p> </p> <p>M1M3_1000N_UL_shape_156.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1000N_UL_shape_156.npy</p> <p>- shape = (5256, 156)</p> <p>- Rows must be FEA nodes, columns must be bending modes.</p> <p>- Not sure what the purpose is of this one.</p> <p><br> </p> <p># M2 Bending modes</p> <p> </p> <p>M2_1um_grid.fits.gz</p> <p>- source = IM/data/M2/M2_1um_grid.DAT</p> <p>- shape = (15984, 75)</p> <p>- Each row is one of 15984 FEA nodes.</p> <p>- 0th column is node index ?</p> <p>- 1st and 2nd columns are FEA node x and y in M2 CS</p> <p>- Last 72 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M2_1um_force.fits.gz</p> <p>- source = IM/data/M2/M2_1um_force.DAT</p> <p>- shape = (72, 75)</p> <p>- Each row is one of 72 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M2 CS</p> <p>- Last 72 columns are forces in Newtons for each mode.</p> <p> </p> <p># M2 print through / thermal</p> <p> </p> <p>M2_GT_FEA.fits.gz</p> <p>- source = IM/data/M2/M2_GT_FEA.txt</p> <p>- shape = (9084, 6)</p> <p>- Each row is one of 9084 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Zenith print through dz coefficient</p> <p>- 3: Horizon print through dz coefficient</p> <p>- 4: z temperature gradient dz coefficient</p> <p>- 5: r temperature gradient dz coefficient</p> <p><br> </p>
Transposable element libraries from 101 fish
<p><span>Repetitive DNA make up a considerable fraction of most eukaryotic genomes. In fish, transposable element (TE) activity has coincided with rapid species diversification. Here, we annotated the repetitive content in 100 genome assemblies, covering the major branches of the diverse lineage of teleost fish. We investigated if TE content correlates with family level net diversification rates and found support for a weak negative correlation. Further, we found that </span><span>TE proportion correlate to genome size, but not to the proportion of short tandem repeats (STRs), </span><span>which implies independent evolutionary paths. </span><span>Marine</span><span> and freshwater fish have large differences in STR content. The most extreme propagation was found in the genomes of codfish species and Atlantic herring. Such a high density of STRs is likely to increase the mutational load, which we propose could be counterbalanced by high fecundity as seen in codfishes and herring.</span></p>
Simulation data of Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468
<p>This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468".</p> <p>Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations.</p> <p>The provided lzip archive is structured into separate folders, one per simulation. Each folder contains the output data and a short README.txt with further hints. For information on the compression algorithm and how to uncompress it lzip please refer to https://en.wikipedia.org/wiki/Lzip.</p>
A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli
<p>Flexible electronics have gained popularity because of their capability to combine softness and functionality. Soft resistive sensors are susceptible to mechanical stimuli and detecting temperature selectively remains a challenge. In this study, soft flexible thermistors are developed for detecting temperature changes based on the positive temperature coefficient (PTC) effect, selectively. By thermomechanical analysis and differential scanning calorimetry, it is observed that thermoplastic elastomers with higher thermal expansion and semicrystalline morphology result in a sensitive thermistor response. To achieve a high sensitivity in temperature and low sensitivity in the detection of mechanical stimulus, a low-carbon filler content is required. The opposite trend is seen for the piezoresistive sensors for mechanical strain detection. Both sensory material types are compatible with thermoplastic material extrusion-based additive manufacturing. The method is used for the fabrication of the sensing elements and an open-source prosthetic hand. The strain sensor detects the bending of the fingers and the temperature sensor detects the temperature when in contact with a heated surface, successfully. In addition, the temperature sensor is used as a tactile sensor to detect contact with a non-heated surface. Combining selective multisensory capabilities will significantly affect the future development of sensorized prosthetic devices and wearable electronics.</p>
Supplementary material for Dynamics of rare earth elements and associated major and trace elements during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation
<p>Database and figures related to the results that are presented in the manusciript Montemagno et al. that will be submitted as research paper in the journal Copernicus/Biogeosciences.</p> <p>Title of the submission: Dynamics of Rare Earth Elements and associated major cations during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation.</p> <p>Authorship: </p> <p>Alessandro Montemagno<sup>1,3</sup>, Christophe Hissler<sup>1</sup>, Johanna Ziebel<sup>2</sup>, Victor Bense<sup>3</sup>, Laurent Pfister<sup>1</sup>, Adriaan J. Teuling<sup>3</sup></p> <p><sup>1</sup>CATchment and ecohydrology research group (CAT/ENVISION/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg</p> <p><sup>2</sup>Biotechnologies and Environmental Analytics Platform (BEAP/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg</p> <p><sup>3</sup>Department of Environmental Sciences, subdivision Hydrology and Quantitative Water Management (HWQM), Wageningen University and Research, Droevendaalsesteeg 4, Wageningen, 6708 PB, The Netherlands</p> <p><em>Correspondence: </em>Alessandro Montemagno (<a href="mailto:alessandro.montemagno@list.lu">alessandro.montemagno@list.lu</a>), Christophe Hissler (<a href="mailto:christophe.hissler@list.lu">christophe.hissler@list.lu</a>)</p>
Data for: Mimicking functional elements of the natural flow regime promotes native fish recovery in a regulated river
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Ultraconserved elements resolve the phylogeny and corroborate patterns of molecular rate variation in herons (Aves: Ardeidae)
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Data from: Taphonomic controls on a multi-element skeletal fossil record
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StoichLife: A global dataset of plant and animal elemental content
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Transposable element libraries from 101 fish
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Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.