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xdslproject/xdsl: v0.31.0
No full textSummary This update of xDSL brings an update to the version of mlir-opt that it is compatible with, we are now compatible with the version distributed with LLVM version 19.1.7. This will hopefully make it easier for users to use tools distributed with LLVM. It also contains some updates to assembly-level dialects and CSL infrastructure. xDSL framework core: add custom repr to BaseAttr and ParamAttrConstraint @superlopuh (#4094) core: better spans for assembly format error messages @superlopuh (#4078) Dialects dialects: (stablehlo) Improve documentation @efferifick (#4096) dialects: (builtin) add custom repr to IntegerType @superlopuh (#4093) dialects: (stablehlo) add more operations @efferifick (#4090) dialects: (bigint) Add start of bigint dialect @EdmundGoodman (#4087) dialects: (vector) add assembly format to vector ops @superlopuh (#4080) dialects (arm): Add FP/SIMD register type @emmau678 (#4034) dialects: (stablehlo) Reorder operations alphabetically @efferifick (#4089) dialects: (vector) fix masked operation class names @superlopuh (#4085) Transformations minor: (csl-stencil-handle-async-flow) Provide task ids as list @n-io (#4077) transforms: (csl) Support sdk 1.3 @n-io (#4076) fix: handle-async-flow to work with no iter_args @dk949 (#4054) Backend backend: (arm) make AssemblyInstructionArg an ABC @superlopuh (#4061) backend: (riscv) use register indices to track available registers @superlopuh (#4062) Documentation documentation: migrate IRDL notebook from jupyter to marimo @compor (#4052) documentation: migrate interoperability notebook from jupyter to marimo @compor (#4059) Continuous Integration CI: Update uv lockfile @github-actions (#4091) CI: Update uv lockfile @github-actions (#4065) Installation pip prod(deps): bump marimo from 0.11.22 to 0.11.23 @dependabot (#4084) pip prod(deps): bump ruff from 0.11.0 to 0.11.1 @dependabot (#4083) pip prod(deps): bump jax from 0.5.2 to 0.5.3 @dependabot (#4082) pip prod(deps): bump pyright from 1.1.396 to 1.1.397 @dependabot (#4081) dependencies: make pre-commit version less strict @superlopuh (#4074) pip prod(deps): bump marimo from 0.11.21 to 0.11.22 @dependabot (#4073) pip prod(deps): bump marimo from 0.11.20 to 0.11.21 @dependabot (#4068) pip prod(deps): bump ruff from 0.10.0 to 0.11.0 @dependabot (#4069) pip prod(deps): bump marimo from 0.11.19 to 0.11.20 @dependabot (#4064) pip prod(deps): bump numpy from 2.2.3 to 2.2.4 @dependabot (#4063) pip prod(deps): bump ruff from 0.9.10 to 0.10.0 @dependabot (#4057) Miscellaneous dependencies: use distributed version of llvm @superlopuh (#4056) bench: add lexing benchmarks and their workloads @EdmundGoodman (#4066) misc: Python dialect codegen - operands and results @mamanain (#4097) misc: add python dialect codegen file with minimal functionality @superlopuh (#4095) fix: (lower-csl-stencil) Send correct size of data chunks @n-io (#4086) minor: (csl-stencil-handle-async-flow) Provide task ids as list @n-io (#4077) fix: handle-async-flow to work with no iter_args @dk949 (#4054) pip prod(deps): bump ruff from 0.10.0 to 0.11.0 @dependabot (#4069) pip prod(deps): bump ruff from 0.9.10 to 0.10.0 @dependabot (#4057)Sasha Lopoukhine, Fehr Mathieu, Emilien Bauer, Anton Lydike, Alex Rice, Nicolai Stawinoga, Michel Weber, Chris Vasiladiotis, Dalia Shaaban, George Bisbas, kingiler, Tobias Grosser, kayode-gif, Joren Dumoulin, Nick Brown, Théo Degioanni, David K, KGrykiel, Prathamesh Tagore, … Alessandro Cerioli. (2025). xdslproject/xdsl: v0.31.0 (v0.31.0). Zenodo. https://doi.org/10.5281/zenodo.1507978
OAIZIB-CM: Dataset from the CartiMorph Project
No full textThis is the official release of OAIZIB-CM dataset. OAIZIB-CM is based on the OAIZIB dataset OAIZIB paper: Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative In OAIZIB-CM, tibial cartilage is split into medial and lateral tibial cartilages. OAIZIB-CM includes CLAIR-Knee-103R, consisting of a template image learned from 103 MR images of subjects without radiographic OA corresponding 5-ROI segmentation mask for cartilages and bones corresponding 20-ROI atlas for articular cartilages It is compulsory to cite the paper if you use the dataset CartiMorph: A framework for automated knee articular cartilage morphometrics For convenient dataset download in Python, please refer to the Hugging Face release of the same dataset: https://huggingface.co/datasets/YongchengYAO/OAIZIB-CMYao, Y. (2025). OAIZIB-CM: Dataset from the CartiMorph Project [Data set]. In CartiMorph: A framework for automated knee articular cartilage morphometrics (v1.0, Vol. 91). Zenodo. https://doi.org/10.5281/zenodo.1493408
Genome-wide motif sites in humans and cattle
No full textThe folder contains genome-wide motif sites for human and cattle. Please cite the following paper if you use the motif sites: https://doi.org/10.1101/2024.08.01.606140 .Zhao, R., & James, P. (2025). Genome-wide motif sites in humans and cattle [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1504671
laurendelong21/clusterMed: Cluster and survival analysis of UK Biobank data reveals associations between physical multimorbidity clusters and subsequent depression - v1.0.0 - February 2025
No full textWhat's Changed Packaging by @laurendelong21 in https://github.com/laurendelong21/clusterMed/pull/1 Bug fixes by @laurendelong21 in https://github.com/laurendelong21/clusterMed/pull/6 Bubble heatmap by @laurendelong21 in https://github.com/laurendelong21/clusterMed/pull/7 Contributors @laurendelong21 @paola-g Full Changelog: https://github.com/laurendelong21/clusterMed/commits/v1.0.0Lauren DeLong, & paola-g. (2025). laurendelong21/clusterMed: Cluster and survival analysis of UK Biobank data reveals associations between physical multimorbidity clusters and subsequent depression - v1.0.0 - February 2025 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.1488277
marianagarciacriado/ArcticPlantDynamics: v1
No full textFirst release of the ArcticPlantDynamics repo.Mariana García Criado. (2025). marianagarciacriado/ArcticPlantDynamics: v1 (Version v1). Zenodo. https://doi.org/10.5281/zenodo.1488449
fmi-faim/faim-ipa: faim-ipa-0.11.1
No full textA collection of Image Processing and Analysis (IPA) functions used at the Facility for Advanced Imaging and Microscopy (FAIM)Tim-Oliver Buchholz, Jan Eglinger, Joel Lüthi, Flurin Sturzenegger, Jesko Wagner, & Allyson Quinn Ryan. (2025). fmi-faim/faim-ipa: faim-ipa-0.11.1 (faim-ipa-0.11.1). Zenodo. https://doi.org/10.5281/zenodo.1484962
Ethnicity coding
No full textThis Zenodo entry details the methodology for extracting and reconciling ethnicity data from the Clinical Practice Research Datalink (CPRD), incorporating both General Practitioner (GP) and Hospital Episode Statistics (HES) sources. The approach aims to resolve discrepancies between these sources and provide a standardized single ethnicity value per patient, categorized into 6 and 12 levels according to NHS coding guidelines. Materials and Methods: Ethnicity data from the CPRD are recorded in multiple formats. This study harmonizes these data to achieve consistent ethnicity classification across patient records, following a hierarchal reconciliation protocol prioritizing hospital data over GP records. Ethnicity Levels: Ethnicity data are processed to conform to two levels of granularity: Six high-level categories: White, Black, Asian, Mixed, Other, Unknown Twelve detailed categories: Bangladeshi, Black African, Black Caribbean, Black Other, Chinese, Indian, Mixed, Other Asian, Other, Pakistani, Unknown, White Source Data Mapping: CPRD Medcodes: Directly mapped to 490 SNOMED codes SNOMED to NHS Codes: SNOMED codes are linked to 26 NHS ethnicity codes NHS to HES Codes: These NHS codes further map into 12 HES hospital ethnicities, which then consolidate into the 6 broad categories mentioned above Algorithm (AIM-CISC): Hospital Data Priority: Ethnicity records from hospital sources override those from GP records unless the hospital data is classified as "Unknown", null, or empty. Conflict Resolution Within GP Data: The frequency of recorded ethnicities determines the selection. The most frequently recorded ethnicity prevails. If frequencies are tied, the most recent record is used. In cases where records are equally recent, the first alphabetically listed ethnicity is selected. Unique Patient Identifiers: Each patient is represented once in hospital data, ensuring a single source of truth for hospital-based ethnicities. This simplifies reconciliation with GP data when discrepancies arise. Source Documentation and References: Reference for Code Lists: Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity (Pineda-Moncusí et al., 2022): https://doi.org/10.1101/2022.11.11.22282217 GitHub Repository for Code Lists: https://github.com/BHFDSC/CCU037_01/blob/main/england/phenotypes/snomed_meaning_and_map_to_primary_code.csv NHS Ethnicity Codes Documentation: https://www.datadictionary.nhs.uk/attributes/ethnic_category_code_2001.html open_in_new Notes on mapping: Instances were noted where multiple Medcodes map back to a single SNOMED code, highlighting the importance of careful data cross-referencing. For example, two different Medcodes represent the New Zealand European ethnicity, which both map back to the identical SNOMED code.Galdi, P., & De Ferrari, L. (2025). Ethnicity coding [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1504438
The NNLO and aN3LO MSHT20xNNPDF40 combined PDF sets
No full textThese PDF sets are obtained by combining 100 Monte Carlo replicas obtained from the MSHT20 and NNPDF4.0 PDF sets. Variants coresponding to nnlo (QCD), an3lo (QCD), nnlo (QCD+QED), and an3lo (QCD+QED) are available in the LHAPDF6 format. All variants correspond to as=0.118.Nocera, E. R. (2025). The NNLO and aN3LO MSHT20xNNPDF40 combined PDF sets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1490806
Photogrammetry model of Jurassic fossil nurse log
No full textPhotogrammetry model of Jurassic fossil nurse log. A ca. 150-million-year-old nurse log was discovered and described from the Jurassic of Scotland, using classic palaeobotanical techniques and microscopy. The wood was collected along the foreshore north east of Helmsdale (58°07'15.8"N 3°37'48.6"W). We interpret our new finding within the evolution of conifers with a literature review of fossil nurse logs, fungal and arthropod evolution. The method to create the photogrammetry model was as follows: Images of thin sections examined at the University of Edinburgh were taken using a Keyence VHX-7000N. The HDR Function of the Keyence VHX-7000N was used to maximise image quality. Macro scale features of the nurse log were taken under cross-polarised light with a Canon EOS 5D Mark IV camera and EF 100mm f/2.8L Macro IS USM lens. This camera and lens set up was also used to capture the 3D structure of the nurse log using photogrammetry. Photographs were taken of the specimen placed on a Genie mini II turntable from Syrp Lab within a 16”x16” Neewer Studio Box lightbox. A scaled model and renders were created using AgiSoft Metashape Professional (64-bit)
To promote network connectivity in colloidal-rod suspensions, end with a tip
No full textData and codes to support the manuscript "To promote network connectivity in colloidal-rod suspensions, end with a tip" ## Abstract - Colloidal gels formed from patchy rods provide a promising platform to design novel functional materials and formulations. Yet, the case for localised interactions at the rod tips remains relatively unexplored. Here we probe the structure and dynamics of such systems by means of coarse-grained computer simulations, and show that the emerging tipped gel networks are fundamentally different to uniform ones. Structurally, tipped networks are better connected, and, unlike in the uniform gels, the connectivity increases with the length of the constituent rods. The dynamics are also fundamentally different, with the gelation time in the tipped networks (contrary to the uniform gels) exhibiting an inverse relationship with rod-length
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