Find research datasets worth reusing

Figure 4 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic

Figure 4: Optimal quantum yield (FV/FM) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient (two weeks; left graph) and post-cultivation at 10 °C (one week; right graph). Horizontal lines represent the median; boxes, the interquartile range; whiskers, 1.5× of inter-quartile range (n = 5).

Figure 3 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic

Figure 3: Photographic documentation of Laminaria digitata and Hedophyllum nigripes sporophytes exposed to a temperature gradient after post-cultivation at 10 °C. Images are not to scale. Triangular cuts marked individual sporophytes per replicate.

Figure 2 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic

Figure 2: Relative growth rates (RGR; % d−1) of Laminaria digitata (top) and Hedophyllum nigripes (bottom) sporophytes in a temperature gradient over the experimental time (14 days; left side of the dotted line) and recovery at 10 °C (one week; right side of the dotted line; n = 5, mean ± SD). Each value denotes the RGR between the indicated time point and the measuring day before.

Figure 1 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic

Figure 1: Standardized growth rates (GR) based on surface area (%) of Laminaria digitata (white dots) and Hedophyllum nigripes (black dots) sporophytes over two weeks in a temperature gradient (n = 5, mean ± SD). Different letters denote significant differences within each species (ANOVA with Tukey's post hoc test: α <0.05, A– D = L. digitata; a–c = H. nigripes). Asterisks indicate significant differences between standardized GR of L. digitata and H. nigripes (two-way ANOVA with Tukey's post hoc test).

Figure 5 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic

Figure 5: Density of gametophytes of Laminaria digitata (A, C) and Hedophyllum nigripes (B, D) at day 7 (A, B) and day 14 (C, D) in temperature gradients between 0 and 25 °C (L. digitata) and 22 °C (H. nigripes) (n = 3–4, mean ± SD). Broken horizontal lines show the mean initial gametophyte density for each species after the acclimatization phase (day 0). †All gametophytes died.

opencc-by-4.0Sep 2024View details →

Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"

This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits" Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions. The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary.

opencc-by-4.0May 2023View details →

Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning

Abstract Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (test-GT) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding test-GT samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.   Methods Manual Generation of test-GT The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience. Segmentation of Coronary Arteries using a Deep Learning Model For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).   Dataset organization The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available: <ul> <li>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater. A sample belonging to the test-GT set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding test-GT sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned test-GT sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>

Additional Data to "A Challenge for Contrastive L1/L2 Corpus Studies"

Additional plots and data to the paper "A Challenge for Contrastive L1/L2 Corpus Studies: Large Inter- and Intra-Individual Variation Across Morphological, but Not Global Syntactic Categories in Task-Based Corpus Data of a Homogeneous L1 German Group", published in Frontiers of Psychology on Nov 25th, 2021. This repository contains the Falko data and additional plots of the individual distributions of morphological noun classes, frequent dependency types, and parts of speech in the Falko data. The Kobalt data discussed in the paper can be found in Shadrova (2021).

opencc-by-4.0Feb 2022View details →

Cellpose model for Digital Phase Contrast images

Name: Cellpose model for Digital Phase Contrast images Data type: Cellpose model, trained via transfer learning from ‘cyto’ model. Training Dataset: Light microscopy (Digital Phase Contrast) and Manual annotations (10.5281/zenodo.5996883) Training Procedure: Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a>  python -m cellpose --train --dir TRAINING/DATASET/PATH/train --test_dir TRAINING/DATASET/PATH/test --pretrained_model cyto --chan 0 --chan2 0 The model file (MODEL NAME) in this repository is the result of this training. Prediction Procedure: Using this model, a label image can be obtained from new unseen images in a given folder with python -m cellpose --dir NEW/DATASET/PATH --pretrained_model FULL_MODEL_PATH --chan 0 --chan2 0 --save_tif --no_npy

opencc-by-4.0Feb 2022View details →

Digital Phase Contrast on Primary Dermal Human Fibroblasts cells

Name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells  Data type: Paired microscopy images (Digital Phase Contrast, square rooted) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>. Microscopy data type: Light microscopy (Digital Phase Contrast and Brighfield ) Manual annotations: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well. Microscope: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective Cell type: Primary Dermal Human Fibroblasts cells File format: .tif (16-bit for DPC and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm) NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )

opencc-by-4.0Feb 2022View details →

Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images

Name: Cellpose models for Brightfield and Digital Phase Contrast images Data type: Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional Training Dataset . Includes corresponding csv files with 'Quality Control' metrics(§) (model.zip). Training Dataset: Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) Training Procedure: The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).   Command Line Execution for the different trained models nuclei_from_bf: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> cyto_from_bf: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre>   nuclei_from_dpc: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> cyto_from_dpc: <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre>   nuclei_from_sqrdpc: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> cyto_from_sqrdpc: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre>   NOTE (§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> . NOTE: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope”  dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images. NOTE: Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.

FIG. 6 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 6. — String of A, fishing-net floats, which, if nonlethally entangled around an actively swimming animal and bobbing up and down on the water surface might be misinterpreted as the long tail of a presumed sea serpent, especially if the UMO was also pulling a "mane" of intertwined fishing-net and accumulations of seaweed or other natural or anthropogenic debris; B, cork pieces used to buoy a gillnet;C, wooden casks used to suspend a purse-seine. Photo credits: R. France (taken at Battle Harbour National Historic District, Battle Harbour, Newfoundland and Labrador [A]; Cape Ann Museum, Massachusetts [B]); nineteenth-century illustration reproduced from France 2019a [C].

FIG. 3 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 3. — Illustrations of the many-humped, string-of-buoys Gloucester Sea Serpent observed by hundreds over a period of weeks in Massachusetts in the nineteenth century. Further details of these sightings as well as other illustrations are presented in France (2019a, b).

FIG. 2 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 2. — Unidentified marine objects seen in waters around Australia in A, 1900 (anecdote 19, Wide World Magazine); B, 1913 (anecdote 28, Sunday Times); C, 1939 (anecdote 67, Cryptozoology); D, 1981 (Papua New Guinea waters; Smith 2020). Anecdote numbers correspond to those from Smith (2020). The illustration of the New Guinea UMO depicts the classic manyhumped, string-of-buoys "sea serpent" seen repeatedly around the world, which could, as has been suggested for other regions, be an actual string of entangled buoys from a fishing net. The 1913 Australian UMO was observed to sport a beard and dorsal crest, both traits which have been suggested to indicate presence of entangled fishing gear for other UMOs. Clearly the coils or loops elevated above the surface of the water that were observed for the 1900 and 1939 UMOs are biologically impossible and consequently represent a train of anthropogenic material.

FIG. 4 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 4. — Pre-plastic maritime equipment forming the backbone of the long tails of putative sea serpents.A-F, early to mid twentieth-century fishing ropes and nets constructed of natural fibre (hemp); G, H, remarkably preserved hemp ropes retrieved from a 400 year-old sunken Basque whaling ship. Photo credits: R. France (taken at the Fisheries Museum of the Atlantic,Lunenburg, Nova Scotia [A, B]; the Maritime Museum of the Atlantic, Halifax, Nova Scotia [C, D]; Battle Harbour National Historic District, Battle Harbour, Newfoundland and Labrador [E, F] [see France 2019a for other, similar photos as well as nineteenth-century illustrations of the same]); Red Bay National Historic Site & UNESCO World Heritage Site, Red Bay, Newfoundland and Labrador (G, H).

FIG. 5 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 5. — Pre-plastic maritime material forming the humps of the long tails of putative sea serpents. A, B, blown-glass balls used as floats from the nineteenth-century; C, D, nineteenth-century cork floats; E, F, wooden casks of the type often used as floats on fishing nets. Photos credits: R. France (taken at the Fisheries Museum of the Atlantic, Lunenburg, Nova Scotia [A, D]; the Battle Harbour National Historic District, Battle Harbour, Newfoundland and Labrador [E, F]); the Mystic Seaport Museum Archive and Collections, Mystic, Connecticut (B, C).

FIG. 1 in From folkloric belief to fishery bycatch: contrasting cryptozoological and euhemeristic interpretations of Australian sea serpents

FIG. 1. — Unidentified marine objects (UMOs) representing the "many-humped" or "string-of-buoys" typology of "sea serpents" observed in A, Géographe Bay, Australia, in 1879; B, the Great Barrier Reef, Australia, in 1934. Further details of these sightings and illustrations of other UMOs from the nearby Western Pacific are presented in France (2020a).

opencc-by-4.0Oct 2021View details →

Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"

Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'' The README.md file includes explanations about the data in the repository.

dryad40/100

Contrasting effects of anthropogenic disturbance on the interaction among sympatric carnivores

Interaction among species is central to the stability of community structure. However, anthropogenic pressures alter interactions, disrupt trophic levels, and threaten ecosystem stability. Understanding interactions across human land-use gradients is fundamental to mitigating anthropogenic threats effectively and better managing threatened species. Using data from a large-scale camera trap survey, we developed a multispecies occupancy model for a carnivore guild comprising tiger, leopard, and dhole to investigate the effects of environmental (forest and prey abundance) and anthropogenic (settlement) variables on the interspecific interaction. Human settlement density had a strong but contrasting effect on interaction: as settlement density increased, tigers and leopards were less likely to coexist whereas leopards and dholes were more likely to occur together. Tiger and dhole occupancy was negatively associated with settlement density whereas, the leopard was positively associated. Per cent forest cover and large prey abundance had ubiquitous positive effects on carnivore occupancy. Our results indicate that human presence alters available niche space and spatial overlap among predators affecting interactions. The duality in the effect of the settlement on interacting pairs suggests that humans create a landscape of fear for apex predators but promotes coexistence between subordinate species partially supporting the intermediate disturbance hypothesis.

opencc-zeroApr 2022View details →