Biomolecular condensates are important organizers of cellular biochemistry. Understanding their structure and function can, for example, shed light on several neurodegenerative diseases. Although biomolecular condensates play such an important role, we barely understand their internal structure. So far, it is known that structures below the optical resolution limit are proposed to exist. These scales are accessible with X-rays, which are well suited to quantifying the organization of molecules through the structure factor. Here, we propose experiments at the BioSAXS BM29 beamline at the ESRF to further unravel the structure of biomolecular condensates. We hope that these experiments not only shed light on our own system, but also again demonstrate the power of X-rays to a growing community of condensate scientists.

Here, we provide sample data to test scripts available at https://github.com/AltmeyerLab/ from pre-imaging (“Live pre-treatment.zip”), post-imaging (“Live post-treatment.zip”) and iterative staining (“Iterative staining.zip”). Additionally, there are .txt files which were extracted from ScanR Analysis (analysis_parameters_live.txt and analysis_parameters_iterative_staining.txt) which can be used to test the matlab scripts. Finally, there is a Tracking_fiji.xls file which is used to copy the values generated from the “Coord” table in the matlab script to import into the tracking plugin in FIJI, so that the tracks can be overlayed with the stack of the corresponding cells.

Here, we provide sample data to test scripts available at https://github.com/AltmeyerLab/ from pre-imaging (“Pre-imaging.zip”), post-imaging (“Post-imaging.zip”) and iterative staining (“Iterative staining.zip”). Additionally, there are .txt files which were extracted from ScanR Analysis (analysis_live.txt and analysis_iterative_staining.txt) which can be used to test the matlab scripts. Finally, there is a Tracking_fiji.xls file which is used to copy the values generated from the “Coord” table in the matlab script to import into the tracking plugin in FIJI, so that the tracks can be overlayed with the stack of the corresponding cells.

targets.zip                    # ML-ready targets from sPlotOpen and gbif POWOpredictors.zip               # ML-ready predictors for sPlotOpen, gbif POWO, and random background points# PO gbif_thinned_powo.csv              # PO: cleaned using POWO rangesgbif_thinned_iucn.csv              # PO: cleaned using IUCN ranges# PAsplotopen.csv              # PA data from sPlotOpen# Range maps├── IUCN/│   ├── original/                      # Original IUCN range maps│   └── per_species_polygons.zip      # Filtered, species-specific polygons│├── POWO/│   ├── per_species_polygons.zip      # Filtered species range maps based on POWO│   └── (...)                        │└── WGSRPD/    ├── level1/                           ├── level2/                           └── level3/                       # Level 3 regions used by POWO

This dataset provides historical and future runoff data for 2,236 catchments in Western Patagonia, along with static catchment attributes. It accompanies the study "Hybrid glacio-hydrological modelling reveals contrasting runoff changes in Western Patagonia over the 21st century" by Aguayo et al. (2025). The dataset includes three files:1. Basin_attributes.csvA tabular dataset where each row (with basin_id) represents one of the 2,236 catchments, and each column corresponds to a specific attribute.Detailed descriptions of each attribute are available in the Table S1 of Supplementary Material.2. Basin_boundaries.gpkgA GeoPackage file containing the polygon boundaries of the 2,236 catchments in vector format.Each feature includes the corresponding basin_id, allowing for spatial linkage with other dataset components.3. Q_historical.ncContains historical daily runoff time series (2000-2019) for each catchment, based on historical climate conditions from PMET-sim (Aguayo et al., 2024).Provided in NetCDF format with dimensions: date, basin_id, and model. 4. Q_future.ncProvides daily runoff projections for 2022-2099 under multiple General Circulation Models (GCMs; n = 5) and Shared Socioeconomic Pathways (SSPs; n = 2).Stored in NetCDF format with dimensions: date, basin_id, model, gcm, and ssp.The runoff time series are based on different modeling approaches, including: hybrid models (LSTM+OGGM), pure deep learning models (LSTM), and process-based coupled glacio-hydrological models (TUWmodel+OGGM and GR4J+OGGM)

Raw data for the project "Development of ArgTag for scalable solid-phase synthesis of aggregating peptides".

This dataset contains coregistered ultrasound and multispectral optoacoustic tomography (MSOT) carotid artery images from a set of 10 participants. MSOT data contains model-based multiwavelength reconstructions and unmixed images of deoxyhemoglobin, oxyhemoglobin, lipids, and water. This data is used for assessing image quality performance and extraction of unmixed values in the carotid.

This dataset contains coregistered ultrasound and multispectral optoacoustic tomography (MSOT) carotid artery images from a set of 10 participants. MSOT data contains model-based multiwavelength reconstructions and unmixed images of deoxyhemoglobin, oxyhemoglobin, lipids, and water. This data is used for assessing image quality performance and extraction of unmixed values in the carotid.

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