This dataset contains a collection of 16x16 RGBA Minecraft assets (blocks and item textures). The assets are standardized in format (RGBA) and resolution (16x16) 13,072 Total Mods 552,234 Total Items 481,701 Total Blocks

  📦 Contents

✅ Resolution: 16x16 pixels ✅ Format: PNG (RGBA) ✅ Types: Block textures Item textures

✅ Color Space: RGBA (with alpha channel)

Field Type Description

image Image The 16x16 RGBA texture, loaded as a PIL Image object.

file_name string… See the full description on the dataset page: https://huggingface.co/datasets/OVAWARE/16xModdedMinecraft.

This database, named UIBVFEDPlus-Light, is an extension of the previously published UIBVFED virtual facial expression dataset. It includes 100 characters, four lighting configurations and 13200 images. Images are in png format with a resolution of 1080x1920 RGB, without alpha channel and an average size of 2.0 MB.
The images represent virtual characters reproducing FACS-based facial expressions. Expressions are classified based on the six universal emotions (Anger, Disgust, Fear, Joy, Sadness, and Surprise) labeled according to Faigin’s classification.
The dataset aims to give researchers access to data they may use to support their research and generate new knowledge. In particular, to study the effect of lighting conditions in the fields of facial expression and emotion recognition.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m. This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region. The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA). Most of the imagery in the composite imagery from 2017 - 2021. Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates. The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together. The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps. To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.
Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.
The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link. The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif. Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5 Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895 Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data. Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data. AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer. World_AIMS_Marine-satellite-imagery The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS. Change Log: 2025-05-12: Eric Lawrey Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation. 2025-02-04: Eric Lawrey Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record. 2023-11-22: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map. 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

This data set contains 4-band ortho-rectified mosaic tiles, created as a product from the NOAA Integrated Ocean and Coastal Mapping (IOCM) initiative. They are 8 bit RGB and NIR band stacked mosaics with an alpha channel. The source imagery was acquired from 20230625 - 20230702 with an Applanix Digital Sensor System (DSS). The original images were acquired at a higher resolution to support t...

Name: HeLa “Kyoto” cells under the scope

Microscope: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera.

Microscopy data type: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21 with a downscaling of 4.

Channel 1 : Low Contrast DPC (Digital Phase Contrast)

Channel 2 : High Contrast DPC

Channel 3 : Brightfield

Channel 4 : EGFP-α-tubulin

Channel 5 : mCherry-H2B

File format: .tif (16-bit)

Image size: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t

Cell type: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz et al, 2010 )

Protocol: Cells were resuspended in Imaging media and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes.

Imaging media: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax.

NOTE: This dataset was used to automatically generate label images in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140064

NOTE: This dataset was used to train the cellpose models in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140111

4 channel images of cells used in the manuscript channel 1: F-Actin channel 2: alpha smooth muscle actin channel 3: DAPI channel 4: Phase-contrast

US Tariff Impact on Market

US tariffs on imported components, particularly semiconductors and networking equipment, could pose challenges for the next-generation intrusion prevention system (NGIPS) market. Many NGIPS solutions rely on these imported parts to build their hardware, and increased tariffs can raise production costs.

This could lead to higher prices for end-users, potentially slowing down adoption rates, especially among small and medium-sized businesses (SMBs) that are more price-sensitive. Additionally, these tariffs may disrupt supply chains, delaying product releases and updates, which could impact companies' ability to stay competitive. Increased costs and potential delays may also affect market leaders, making it difficult for them to offer affordable and timely solutions.

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Impact on Sectors (Tariff Percentage Impact)

• Hardware Components (5-7%)
• Networking Equipment (4-6%)
• Cloud Services (3-5%)

Economic Impact

US tariffs could increase production costs for NGIPS companies, particularly on hardware components such as semiconductors and networking equipment. This could lead to higher prices for end-users, which might reduce demand from price-sensitive businesses, especially in SMBs. The increased costs may also reduce profitability in the short term.

Geographical Impact

The US market for NGIPS may experience slower growth due to tariffs affecting key components sourced internationally. This could increase the price of security solutions, especially in North America. Conversely, other regions, such as Asia-Pacific, where tariff barriers are lower, might experience faster adoption and expansion of NGIPS technologies.

Business Impact

The imposition of tariffs could limit the ability of NGIPS companies to deliver affordable and timely solutions. Increased production costs could lead to higher prices for security systems, reducing demand in cost-sensitive sectors. Additionally, delays in product development due to tariff-related disruptions could harm market competitiveness, especially in North America.

Wetted channel networks expand and contract throughout the year. Direct observation of this process can be made by multiple intensive surveys of a catchment throughout the year. Godsey et al. (2014) suggest that the extent of the wetted channel network scales with discharge at the outlet by a power law (L = αQ^β). Using this relationship, we developed a framework to assess variability in the extent of wetted channels as a function of beta, β, and the variability in streamflow, Q (Lapides et al. 2021). This resource includes the empirical basis for the study and data compiled from the literature and maps.

1 - Channel length survey data (csv files) 2 - Discharge time series data (csv files) 3 - Watershed metadata (csv file) 4 - Blueline network files (pdf, png, and shp files)

This collection includes all watersheds where at least three channel length surveys have been conducted and where a corresponding discharge time series dataset is available. The requirement of a minimum of three channel length surveys stems from the data requirements to find alpha, α, and β for the power law relationship between discharge and stream network length for headwater catchments (Godsey et al. 2014). Data for 14 watersheds worldwide are included, along with watershed metadata, reference maps, shapefiles and a composite of USGS blueline stream network imagery with terrain for watersheds of interest in the United States.

Methods used to process the datasets or create other assets in this collection are included in the abstracts or additional metadata for each of the four resources listed above. Python code used to process data, compute variables, and create graphics is available at: https://zenodo.org/record/4057320

Stickers is a dataset consisting of 577 high-quality sticker images with alpha channel.

Training and test datasets for the deep learning model created in: A Deep Learning Approach to Identify and Segment α-Smooth Muscle Actin Stress Fiber Positive Cells. Contains 300, 3 channel training images (red: F-actin, green: a-SMA, blue: DAPI), rgb composite images, and segmentation labels (label 3 classes: background, a-SMA SF-, and a-SMA SF+)

This CellProfiler 3.1.9 pipeline creates a maximum intensity z-projection of the image stack from each channel. (CPPROJ)

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron’s calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal. Methods Generation of transgenic Hydra

Following the method previously described, a transgenic Hydra vulgaris (strain AEP) line was established to express GCaMP7s in the cytosol and tdTomato in the nucleus of cells derived from the interstitial stem cell lineage. The plasmid (Addgene catalog no. 102558) was modified by replacing the actin promoter with the EF1-alpha promoter and inserting DsRed downstream of the GCaMP6s sequence. To ensure the nuclear localization of DsRed, a P2A self-cleaving peptide sequence containing a nuclear localizing signal (cccaagaagaagaggaaggtg) was inserted between GCaMP6s and DsRed. The nuclear localization of DsRed was confirmed by electroplating the plasmid into Hydra. To enhance fluorescence intensity, GCaMP6s was replaced with jGCaMP7s and DsRed was replaced with tdTomato. Finally, for microinjections, the EF1-alpha promoter was replaced with the Actin promoter. All fluorescent reporter gene sequences were codon optimized specifically for Hydra. Standard embryo microinjection of this plasmid was performed and transgenic hatchlings expressing cytoplasmic GCaMP7s and nuclear tdTomato in the interstitial cell lineage were isolated. Transgenic animals were bred until all neurons were expressing both cytoplasmic GCaMP7s and nuclear tdTomato. Transgenic Hydra were cultured using standard methods in Hydra medium (1 mM calcium chloride dehydrate, 0.33 mM magnesium sulfate anhydrous, 0.5 mM sodium bicarbonate, 0.03 mM potassium chloride) at 18 °C on a 12 h light/12 h dark cycle. They were fed freshly hatched Artemia nauplii twice per week.

Imaging

Dual-labeled transgenic Hydra were prepared for imaging as described. Imaging was performed using a custom dual-channel spinning disc confocal microscope (Solamere Yokogawa CSU-X1) with a sCMOS camera for each channel (Teledyne-Photometrics Prime-BSI). Samples were simultaneously illuminated with both 488 nm and 561 nm lasers (Coherent OBIS). Emission light was split with a dichroic mirror sending green light to one camera and red light to the other. Cameras were aligned with pinholes and images were registered during processing. Images were captured with a frame rate of 10 frames per second using a 6X objective (Navitar HR Plan Apo 6X/0.3) and Micro-Manager software.

Immunostaining

Immunostaining was performed using the previously established protocol optimized for double-labeling experiments in Hydra with the anti-Hydra cadherin antibody, using the following antibodies: Primary antibodies: Hydra cadherin antibody (rabbit) 1:1000 (Thomas Holstein, Heidelberg), anti-tdTomato (goat) 1:200 (Origene, AB8181-200); Secondary antibodies: Alexa 488 donkey anti-rabbit 1:1000 (ThermoFisher A-21206), Rhodamine Red-X donkey anti-goat 1:250 (Jackson 705–295-147). Fixed and stained animals were imaged on the same spinning disc confocal setup described above. Image stacks were taken with a 40X objective (Olympus LUMPlanFl/IR 40X/0.8 W) every 0.5 μm and processed with ImageJ. Images shown are maximum intensity projections of short stacks (5–20 μm).

Segmentation and tracking of nuclei (ByoTrack)

Segmentation of neuronal nuclei

To learn a segmentation model and validate our experiments, we sampled 20 tdTomato images of Hydra on various positions of the animal (e.g., contracted, elongated). We manually annotated these images using the ImageJ draw tool resulting in 12,705 segmented neurons (~ 600/image).

To measure the segmentation performance, 5 random images were kept as a testing dataset. To account for performance variability, we repeated this process 5 times with different seeds, resulting in different training and testing images. We measured the performance with each seed and report the results as (mean ± std).

We used standard instance segmentation metrics (precision, recall, f1-score). But we did not base these metrics on IOU association, where a predicted instance is considered to be a true positive if it overlaps with a ground truth on a sufficiently large area. We rather based these metrics on distance association, where a predicted instance (spot) is considered valid if its mass-center is sufficiently close to the mass-center of a ground truth instance. This has two benefits: first, it is more meaningful for tracking as we only use the position of each detected instance. Second, it allows fair comparison with detection methods that do not try to produce a faithful segmentation or bounding box of the instances like the wavelet thresholding method.

The remaining 15 images were used to train and tune all the parameters of the segmentation model. We also measured the impact of the training data quantity by training the model with fewer images, from 1 to 15.

We compared two detection methods: an analytical method based on wavelet decomposition and thresholding, and a deep learning approach, StarDist.

Calibrating wavelet thresholding method

The method has only two hyperparameters: the scale of the spots and the noise threshold. Both can be easily tuned by hand on a single image. Nonetheless, to be fully autonomous, the system performs a grid search on the training images and chooses the best performing ones. No further training is required. Moreover, to reduce the number of false positives, the neurons with less than 5 pixels area were filtered out.

Training DL method (StarDist)

The deep neural network must be trained and the hyperparameters tuned. We therefore split the training images into training and validation as most standard deep learning approaches do. 20% of the training images were used as validation images. With less than 5 training images, one image was kept for validation. Therefore, the procedure that we show here requires at least 2 training images, one to train the weights of the network, the other to validate the hyper parameters.

The official implementation of StarDist (https://github.com/stardist/stardist) was used to train, validate, and evaluate the performances with our dataset. For tracking, we provide a wrapper to perform the detection process using a trained StarDist model (https://partage.imt.fr/index.php/s/npwHJHZebxqGMPi).

Generating tracklets with probabilistic eMHT

The eMHT algorithm is implemented on the open-source imaging platform Icy (plugin Spot Tracking), which is coded in Java language. Instead of translating all the code into Python, we decided to directly call the Spot Tracking plugin in Icy in a headless mode from the TraSE-IN platform. To ensure compatibility with Icy, we implemented different functions for inputs (detections) and outputs (tracks) wrapping.

We believe that calling implemented tracking solutions in major imaging platforms such as Icy and ImageJ is an efficient and sustainable solution because the main imaging platforms are open-source, the tracking plugins (e.g., Spot Tracking in Icy, Trackmate in ImageJ) are regularly updated by developers, wrapping input and output variables in TraSE-IN is much less tedious and prone to implementation errors than translating all the Java code of tracking methods in Python, and TraSE-IN will easily integrate new tracking solutions developed on other platforms, if needed.

Stitching tracklets

To stitch tracklets obtained with eMHT tracking, we implemented a cost-based algorithm. Briefly, a cost between all the tracklets is computed, and stitched tracklets correspond to the minimal global cost of all tracklets’ linking. Computing the minimum global cost corresponds to a linear assignment problem that we solved using the Jonker-Volgenant algorithm. The cost between tracklets was computed following the EMC algorithm. Input parameters of the stitching function (byotrack.implementation.refiner.stitching.emc2) are the smoothness α of the Thin Plate Spline interpolation and the non-linking cost η (paid for unlinked tracklets). In our tracking scenario (Fig. 4C), we set α = 10 to provide enough regularization to be robust to eMHT linking errors and set non-linking cost η = 5 pixels.

Linking calcium activity to tracked nuclei

To solve the misalignment between nuclei and calcium signal, we implemented a sub-ROI tracking method. For each nucleus, we extracted a 25 × 25 pixels ROI centered on each tracked nucleus. Then, to find neuronal candidates in the GCaMP7s channel, we iteratively computed η local maxima within each ROI (η = 5 typically) and identified the most probable calcium signal. For this, we first applied a gaussian smoothing (σ = 1 pixel) and first identified the global maximum in the ROI. We then deleted the pixels in the neighborhoods of this maximum and reiterated the process η times. To bias extracted maxima towards the nucleus position (and avoid selecting an outlier GCaMP7s signal) we weighted the ROI intensities with Gaussian prior centered on the nucleus position (σ = 5 pixels).