Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

Dataset for Direct Geometric Probe of Singularities in Band Structure

Included here is the processed data illustrated in the figures of both the main text, and the supplemental material. Below is a description of each file's contents.   Figure2Dcode.m contains the MATLAB code that generates Figure 2D of the main text. It takes the band populations inferred from five iterations of measurements, and calculates the means and standard errors for data taken at each theta as defined in the main text.   Figure2Ddata.csv contains the data illustrated in Figure 2D of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/12 radians for each column that follows. Row 5 is the error for the n=1 population, row 6 is the error for the n=2 population, and row 7 is the error on the sum of the population in bands with index not equal to 1 or 2.   Figure3Bcode.ipynb contains the jupyter notebook that generates Figure 3B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be the intermediate quasi-momenta, and different colors are used to differentiate between different acceleration times.   Figure3Bdata.csv contains the data illustrated in Figure 3B of the main text. The five columns correspond to the five different trajectory evolution times (0.5, 0.9, 1.3, 1.7, 2.1 milliseconds) shown in the Figure 3B. The first nine rows correspond to the nine trajectory midpoint positions in the Brillouin zone, as showed in Figure 3A; the first row corresponds to a midpoint at K. The next nine rows are the errors on the measurements.   Figure4Ccode.m contains the MATLAB code that generates Figure 4C of the main text. It takes the band populations inferred from twelve iterations of measurements, each at a different theta as defined in the main text, and calculate the means and standard errors for data taken at each theta.   Figure4Cdata.csv contains the data illustrated in Figure 4C of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/6 radians for each column that follows. Row 11 is the error for the n=3 population, row 12 is the error for the n=4 population, and row 13 is the error on the sum of the population in bands with index not equal to 3 or 4.   FigureS3Bcode.m contains the MATLAB code that generates Figure S3B of the main text. It takes the band populations inferred from seven iterations of measurements, and calculates the means and standard errors for data taken for each hold time at quasi-momentum Q as defined in the main text. The result is then fitted to a sine with exponentially decaying envelope. push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in basic_fcts.py. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data. A decay to account for coherence loss is added.   FigureS3data.csv contains the data illustrated in Figure 3 of the supplementary material. The first row is the data values, and the second row are the error bars.   FigureS4Bcode.m contains the MATLAB code that generates Figure S4B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be acceleration time, and different colors are used to differentiate between different intermediate points. push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the paths in q-space. The Hamiltonian is calculated in basic_fcts.py. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data.   FigureS4data.csv contains the data illustrated in Figure 4 of the supplementary material. The first five rows are the normalized ground band population for five different trajectory mid points on the K - M - K' line of the Brillouin zone.; the first row is for a midpoint at K, and the fifth row is for a midpoint at M. The columns give the trajectory traversal times; the first column corresponds to a traversal time of 0.1 ms and each column corresponds to a new traversal time incremented by 0.2 ms. Rows 6-10 are the error bars for the measurements.   FigureS5Bcode.zip contains the codes that generate Figure S5B of the main text. For each subplots in Fig.S5B, the corresponding MATLAB code in the zip file takes the band populations inferred from three iterations of measurements, and calculate the means and standard errors for data taken at each acceleration time.   FigureS5Bdata.csv contains the data illustrated in Figure 5B of the supplementary material. Rows 1-20 correspond to subpanel (iii) in the Figure S5 of the supplementary material.  Rows 21-40 correspond to subpanel (ii) in the Figure S5 of the supplementary material.  Rows 31-60 correspond to subpanel (i) in the Figure S5 of the supplementary material. Rows 1-10 correspond to the band index and give the normalized band population; row 1 corresponds to band index n=1 and row 10 corresponds to band index n=10. Rows 21-30 correspond to the band index and give the normalized band population; row 21 corresponds to band index n=1 and row 30 corresponds to band index n=10. Rows 41-50 correspond to the band index and give the normalized band population; row 41 corresponds to band index n=1 and row 50 corresponds to band index n=10. Rows 11-20 (31-40) [51-60] give the error in the band populations for measurements in panel iii (ii) [i].   FigureS5Cdata.csv contains the data illustrated in Figure 5C of the supplementary material. The first (second) column is the vertical (horizontal) axis. The fourth (third) column is the error in the points on the vertical (horizontal) axis.   push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in basic_fcts.py. In figure S6A, at each point in time shown the state is projected onto the instantaneous eigenbasis and the different band populations are extracted. In figure S6B and figure S6C, the whole experiment sequences corresponding to figure 2 and figure 4 in the main text are simulated, and the final population obtained is plotted, with the measurement results copied for reference.   FigureS7code.nb contains the mathematica notebook that generate Figure S7 of the main text. This code uses the two-band model described in the supplemental material to perform simulation.   Image_fitting.zip contains the MATLAB code and functions that were used to analyze the band mapping images. multiboxFit_v7_1.m is the main code that uses other MATLAB functions in the zip file. Overall, it takes absorption images as input, finds the position of each peak (BoxGenerator_v1_0.m), fit for the population in each peak in the images (createFit2D.m), assign the correct band number given the final quasi-momentum in the sequence (BoxesBandsThing_v2.m), and finally plot the inferred band populations, along with a visualization of the original images overlain with a Brillouin zone (PlotBZ_v2.m). The result of fits are saved in a separate file that are accessed by other analysis codes. Figure S2 and Figure S5C are also generated with this code.   Additional codes Q_path_BZ.py, group_velo.py & diffr_img.py are included in Full Hamiltonian simulation.zip to ensure the correct functionality of the codes included.

opencc-by-4.0Jul 2022View details →

Direct RNA targeted in situ sequencing for transcriptomic profiling in tissue

You can find here the Direct RNA In Situ Sequencing (HybISS-based) maps generated using the Hight Sensitivity kit from CARTANA AB. They include half a mouse brain coronal section, targeting 50 genes. Genes were targeted in a sequential manner. Both reads, DAPI staining and segmented cells are included. The analysis of the same cells, but using 10X magnification are also provided in an anndata object.

opencc-by-4.0Jul 2022View details →

Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"

Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe

opencc-zeroAug 2022View details →

Attempting genetic inference from directional asymmetry during convergent hindlimb reduction in squamates

Loss and reduction of paired appendages is common in vertebrate evolution. How often does such convergent evolution depend on similar developmental and genetic pathways? For example, many populations of the Threespine Stickleback and Ninespine Stickleback (Gasterosteidae) have independently evolved pelvic reduction, usually based on independent mutations that caused reduced Pitx1 expression. Reduced Pitx1 expression has also been implicated in pelvic reduction in manatees. Thus, hind limb reduction stemming from reduced Pitx1 expression has arisen independently in groups that diverged tens to hundreds of millions of years ago, suggesting a potential for repeated use of Pitx1 across vertebrates. Notably, hindlimb reduction based on reduction of Pitx1 expression produces left-larger directional asymmetry in the vestiges. We used this phenotypic signature as a genetic proxy, testing for hindlimb directional asymmetry in six genera of squamate reptiles that independently evolved hindlimb reduction and for which genetic and developmental tools are not yet developed: Agamodon anguliceps, Bachia intermedia, Chalcides sepsoides, Indotyphlops braminus, Ophisaurus attenuatuas and O. ventralis, and Teius teyou. Significant asymmetry occurred in one taxon, Chalcides sepsoides, whose left-side pelvis and femur vestiges were 18% and 64% larger than right-side vestiges, respectively, suggesting modification of Pitx1 expression in that species. However, there was either right-larger asymmetry or no directional asymmetry in the other five taxa, suggesting multiple developmental genetic pathways to hindlimb reduction in squamates and vertebrates more generally.

opencc-by-4.0Jul 2022View details →

The impact of cerebellar transcranial direct current stimulation (tDCS) on sensorimotor and inter-sensory temporal recalibration

Data related to the study "The impact of cerebellar transcranial direct current stimulation (tDCS) on sensorimotor and inter-sensory temporal recalibration".

opencc-by-4.0Aug 2022View details →

Supplementary Material for Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2

Supplementary Material for "Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2"

opencc-zeroAug 2022View details →

Disentangling direct and indirect drivers of farmland biodiversity at landscape scale

To stop the ongoing decline of farmland biodiversity there are increasing claims for a paradigm shift in agriculture, namely from conserving and restoring farmland biodiversity at field scale (α-diversity) to doing it at landscape scale (γ-diversity). However, knowledge on factors driving farmland γ-diversity is currently limited. Here, we quantified farmland γ-diversity in 123 landscapes and analysed direct and indirect effects of abiotic and land-use factors shaping it using structural equation models. The direction and strength of effects of factors shaping γ-diversity were only partially consistent with what is known about factors shaping α-diversity, and indirect effects were often stronger than direct effects or even opposite. Thus, relationships between factors shaping α-diversity cannot simply be up-scaled to γ-diversity, and also indirect effects should no longer be neglected. Finally, we show that local mitigation measures benefit farmland γ-diversity at landscape scale and are therefore a useful tool for designing biodiversity-friendly landscapes.

opencc-by-4.0Dec 2021View details →

Data for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry

This is data used for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry in the Cryosphere. The code that reproduces this data can be found at <pre>10.5281/zenodo.6908645</pre> See README.md for further instructions.

opencc-zeroAug 2022View details →

Low water availability enhances volatile-mediated direct defenses but disturbs indirect defenses against herbivores

1. Interactions between plants and natural enemies of insect herbivores influence plant productivity and survival by reducing herbivory. Plants attract natural enemies via herbivore-induced plant volatiles (HIPVs), but how water availability (WA) influences HIPV-mediated defenses is unclear. 2. We use tomato (Solanum lycopersicum), tomato fruitworm (Helicoverpa zea), and two natural enemies, the parasitoid wasp (Microplitis croceipes) and the predator spined soldier bug (Podisus maculiventris), to investigate the effect of WA on HIPV emission dynamics and associated plant defense. 3. We show that low WA initially increases total HIPV emission by tomatoes on the first day of herbivore exposure and, in contrast, reduces HIPV emission on the second day. Low WA enhances HIPVs that are mostly found in tomato trichomes. Notably, some volatiles inhibited by low WA are known attractants of natural enemies. Evidence from Y-tube and in-cage behavioral assays indicates that changes in HIPV emissions by low WA compromise the ability of tomato plants to attract natural enemies. 4. Synthesis: Based on our results, we propose a hypothesis where plants respond to low WA by enhancing repellent HIPV emissions and reducing the emission of HIPVs that attract natural enemies, which disrupts natural enemy-mediated plant indirect defenses but enhances plant direct defense against herbivores.

opencc-by-4.0Sep 2022View details →

Paleomagnetic directions, anisotropy of magnetic susceptibility (AMS), and anisotropy of anhysteretic remanent magnetization (AARM) from IODP Sites U1507 and U1511 (Exp. 371, Tasman Sea).

We present here paleomagnetic and magnetic anisotropy data from International Ocean Discovery Program (IODP) Sites U1507 and U1511 (Expedition 371, Tasman Sea). Data consist of three tables that contain: (1) the characteristic remanent magnetization (ChRM) directions, before and after correction for inclination flattening of magnetic remanence, for both sites (Table S2); (2) the anisotropy of magnetic susceptibility (AMS) data from Site U1507 (Table S3); (3) the anisotropy of anhysteretic remanence (AARM) from Site U1507 (Table S4).

opencc-zeroSep 2022View details →

Direct and indirect phenotypic effects on sociability indicate potential to evolve

The decision to leave or join a group is important as group size influences many aspects of organisms' lives and their fitness. This tendency to socialise with others, sociability, should be influenced by genes carried by focal individuals (direct genetic effects) and by genes in partner individuals (indirect genetic effects), indicating the trait's evolution could be slower or faster than expected. However, estimating these genetic parameters is difficult. Here, in a laboratory population of the cockroach Blaptica dubia, I estimate phenotypic parameters for sociability: repeatability (R) and repeatable influence (RI), which indicate whether direct and indirect genetic effects respectively are likely. I also estimate the interaction coefficient (Ψ), which quantifies how strongly a partner's trait influences the phenotype of the focal individual and is key in models for the evolution of interacting phenotypes. Focal individuals were somewhat repeatable for sociability across a three-week period (R = 0.080), and partners also had marginally consistent effects on focal sociability (RI = 0.053). The interaction coefficient was non-zero, although in the opposite sign for the sexes; males preferred to associate with larger individuals (Ψmale = -0.129) while females preferred to associate with smaller individuals (Ψfemale = 0.071). Individual sociability was consistent between dyadic trials and in social networks of groups. These results provide phenotypic evidence that direct and indirect genetic effects have limited influence on sociability, with perhaps the most evolutionary potential stemming from heritable effects of the body mass of partners. Sex-specific interaction coefficients may produce sexual conflict and the evolution of sexual dimorphism in social behaviour.

opencc-by-4.0Dec 2021View details →

Text-fig. 7. SEM (a) and SRXTM (b–e) images of Miranthus kvacekii sp. nov.; Mira locality, Portugal. a: Lateral view of flower bud showing corolla lobes extending beyond calyx; note surface of pedicel, calyx and corolla with small equiaxial epidermal cells and indumentum of densely spaced, short stiff trichomes. b, c: Longitudinal sections through floral bud in two directions perpendicular to each other (a, orthoslice yz1024; b, orthoslice xz0950) showing corolla (co), calyx (ca), stamens (st) and semi-inferior ovary with thin ovary wall (ow) and central mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). d, e: Transverse sections through floral bud above placenta (d, orthoslice xy0915; e, orthoslice xy1095) showing calyx (ca), corolla (co), ovary wall (ow) and ovules (ov); yellow outlines indicate the positions of anthers (d) and filaments (e); orange outlines indicate the position of three of the possible staminodes. Specimen, Mira 100-S170157 (a–e, holotype). Scale bars = 600 µm (a–c), 300 µm (d, e). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications

Text-fig. 7. SEM (a) and SRXTM (b–e) images of Miranthus kvacekii sp. nov.; Mira locality, Portugal. a: Lateral view of flower bud showing corolla lobes extending beyond calyx; note surface of pedicel, calyx and corolla with small equiaxial epidermal cells and indumentum of densely spaced, short stiff trichomes. b, c: Longitudinal sections through floral bud in two directions perpendicular to each other (a, orthoslice yz1024; b, orthoslice xz0950) showing corolla (co), calyx (ca), stamens (st) and semi-inferior ovary with thin ovary wall (ow) and central mushroom-shaped globose placenta (pl) bearing numerous ovules (ov). d, e: Transverse sections through floral bud above placenta (d, orthoslice xy0915; e, orthoslice xy1095) showing calyx (ca), corolla (co), ovary wall (ow) and ovules (ov); yellow outlines indicate the positions of anthers (d) and filaments (e); orange outlines indicate the position of three of the possible staminodes. Specimen, Mira 100-S170157 (a–e, holotype). Scale bars = 600 µm (a–c), 300 µm (d, e).

opencc-by-4.0Dec 2021View details →

Text-fig. 2. E-W cross section of the Urema Graben from Gorongosa to Inhaminga adapted from Flores (1973: fig. 5). Note that in this schema the Mazamba Sandstone directly overlies the Cheringoma Limestone. I.P.CO No. 5 is a bore hole. Vertical exaggeration ×10. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique

Text-fig. 2. E-W cross section of the Urema Graben from Gorongosa to Inhaminga adapted from Flores (1973: fig. 5). Note that in this schema the Mazamba Sandstone directly overlies the Cheringoma Limestone. I.P.CO No. 5 is a bore hole. Vertical exaggeration ×10.

opencc-by-4.0Oct 2022View details →

Data set for "Cortical sensory processing across motivational states during goal-directed behavior"

Data set for: Matteucci G, Guyoton M, Mayrhofer JM, Auffret M, Foustoukos G, Petersen CCH, El-Boustani S, Cortical sensory processing across motivational states during goal-directed behavior (2022). Neuron https://doi.org/10.1016/j.neuron.2022.09.032 There are 2 files in this upload: 1. The file named "Matteucci2022.pdf" is the Open Access pdf file of the manuscript published in Neuron. 2. The file named "Matteucci_data_code.zip" (~26.5 GB) is a zipped version of a folder "Matteucci_data_code" (~33 GB), which contains the data analysed in the study along with Matlab code used to generate all main figures of the paper. The analysis code is in a subfolder named "code". This subfolder in turn has three subfolders "analysis_scripts", “analysis_functions” (containing the original code for intermediate data processing) and “paper_figures_scripts” (containing the code for generating each figure panel from pre-processed data). The main script “reproduce_figures.m” will call the subscripts contained in the  “paper_figures_scripts” folder to reproduce the plots contained in all main figures of the paper (and take care of adding the relevant code and data folders and subfolders to Matlab file path). The raw and pre-processed data analysed in the study can be found in the folder named "data". A “README.txt” file provides further details on the content of each subfolder.

opencc-by-4.0Oct 2022View details →

Data from "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction"

Sediment data from Steart Marshes described in Mossman et al. "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction". Data are provided as a .xlsx file (Data package.xlsx) with four tabs. Tab 1 has column heading descriptions. Tab 2 has total carbon samples. Tab 3 has total organic carbon samples. Tab 4 has bulk density samples. Each tab is also provided as a seperate csv file.

opencc-by-4.0Aug 2017View details →

Characterization data - Direct and Stereospecific [3+2] Synthesis of Pyrrolidines from Simple Unactivated Alkenes

Primary data of all the new compounds reported in the publication DOI:10.1002/anie.201706682 NMR FID files HRMS data

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be used to anticipate the position of the obstacles and make better decisions in order to reach the desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the value of the potential at all other points is positive.