https://researchdata.se/en/catalogue Search results en Description: The dataset contains the experimental and numerical data for the publication titled "A thermally shielded acoustofluidic device for robust particle focusing" by Corato et al, Ultrasonics 2026. The code necessary to analyse and plot the data is also included. The study investigates temperature regulation of acoustofluidic chips and how this affects the separation performance of small particles. Mon, 11 May 2026 09:38:06 GMT https://researchdata.se/en/catalogue/dataset/2026-113 https://researchdata.se/en/catalogue/dataset/2026-113 Lund University Enrico Corato Per Augustsson This item contains reference databases, benchmarking resources, and a reproducibility container associated with CoMR (Comprehensive Mitochondrial proteome Reconstruction),an integrative workflow for reconstructing mitochondrial proteomes from eukaryotic protein sequence data. Mitochondrial proteome reconstruction often relies heavily on prediction of mitochondrial targeting signals (MTSs), but MTS predictors are mainly trained on model organisms and may perform poorly in phylogenetically divergent lineages or in organisms with atypical or reduced targeting sequences. CoMR was developed to address this by integrating complementary evidence sources within a unified scoring framework, including targeting prediction, curated homology searches, large-scale similarity searches, profile HMM detection, and automated phylogenetic analysis. The workflow is implemented as a modular **Snakemake-based pipeline** and is distributed in containerized form to support reproducible execution across computing environments. The files deposited here support inspection, reuse, and reproducibility of that workflow. They include: (1) CoMR databases with FASTA databases, preformatted BLAST resources, orthogroup alignment archives, and HMM profile archives; (2) a benchmarking collection with filtered FASTA and DIAMOND databases, benchmark proteomes, benchmarking scripts, summary tables, figures, and benchmarking outputs; and (3) a Singularity/Apptainer container image (CoMR.sif) for running CoMR in a controlled computational environment. The benchmarking material corresponds to the analyses described in the paper for the model yeast *Saccharomyces cerevisiae* and the divergent anaerobic protist *Paratrimastix pyriformis*. In the manuscript, CoMR achieved strong discriminatory performance in yeast (ROC-AUC 0.92), exceeding standalone TargetP2 prediction (ROC-AUC 0.72), and maintained robust performance in *P. pyriformis* (ROC-AUC 0.86), where precision-recall analysis also supported improved recovery of mitochondrial-related organelle proteins relative to TargetP2. The benchmarking resources in this deposit include the processed data,scripts, figures, and output archives underlying those comparisons. The deposited reference resources include the **CoMR Subtractive Mitochondrial Database (SMD)**, supporting HMM resources, and benchmarking-modified database versions generated for performance evaluation with taxonomic exclusion to reduce circularity. The benchmarking directory also documents how filtered databases and orthogroup alignments were generated, and how benchmarking tables, ROC curves, and precision-recall summaries were generated from CoMR output tables. The accompanying README and MANIFEST files provide a self-contained guide to the files and an inventory of the distributed content. Tue, 21 Apr 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-31361839 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-31361839 Lund University Julie Boisard Shelby Williams Andrew J. Roger Courtney Stairs The dataset contains transcriptome sequencing (RNA-seq) data of 704 soft tissue tumors (STT) from 704 patients. The sequencing data comes in FASTQ format and contains 1408 files. A total of 56 different STT types were included in the present study and each tumor type was represented by >2 samples. The vast majority of the patients had been diagnosed and treated at the sarcoma centers in Lund (Sweden) or Stockholm (Sweden) during the period 1988-2020, but also a few samples from other centers, obtained through previous collaborative projects, were included. RNA was extracted from fresh frozen tumor material and samples were primary lesions unless otherwise indicated. The sequencing was performed using 2x150 bp paired-end chemistry on a series of Illumina instruments at various facilities during the period 2013-2023.Samples were obtained after informed consent from the patients and the study was approved by the regional ethics committee (Etikprövningsmyndigheten, Uppsala, dnr 2023-01550-01, dnr 2025-02997-02). This dataset is included in the study "Transcriptomic subgroups in soft tissue tumors correlate with morphologic subtype, genomic features, and outcome" (http://identifiers.org/ega.study:EGAS50000001472). Mon, 26 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/fega-sweden-egad50000002120-html https://researchdata.se/en/catalogue/dataset/fega-sweden-egad50000002120-html Lund University Quartz XRD data Thu, 01 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-48391-83d531de-38fc-449a-af68-465cd81ae285 https://researchdata.se/en/catalogue/dataset/doi-10-48391-83d531de-38fc-449a-af68-465cd81ae285 Lund University Sanna Alwmark Jungfraujoch is a single-unit server equipped with heterogeneous FPGA-GPU Architecture that is capable of performing 38 GB/s real-time crystallography data acquisition with PSI JUNGFRAU 9M pixel detector operating at 2 kHz, online data processing from spot finding to indexing at 2 kHz, and enabled optional smart data reduction based on the on-the-fly indexing results while the data keep streaming. These data are obtained and analyzed to show the indexing at 2 kHz works reliably. One dataset is collected with the double crystal monochromator (DCM) and the other is collected with multilater monochromator (MLM). Data collected with DCM: /data/visitors/micromax/20230350/20240521/raw/LysozymeJet8/LysozymeJet8-lysozyme1/LysozymeJet8-lysozyme1_1_master.h5 - Flux: ~10e12 ph/s Data collected with MLM: /data/visitors/micromax/20230350/20240521/raw/run2/LysozymeJet5/LysozymeJet5-lysozyme1/LysozymeJet5-lysozyme1_2_master.h5 - Flux: ~10e14 ph/s Thu, 01 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-48391-b46ec7ab-f346-421f-83e2-cdf251d1e7fa https://researchdata.se/en/catalogue/dataset/doi-10-48391-b46ec7ab-f346-421f-83e2-cdf251d1e7fa Lund University Jiaxin Duan, Hans-Christian Stadler, Jie Nan, Zdenek Matěj, Guillaume Gotthard, Florian Dworkowski, Monika Bjelčić, Oskar Aurelius, Aldo Mozzanica, Sascha Grimm, Max Burian, Thomas Ursby, Meitian Wang, Filip Leonarski The exponential growth in data generation from advanced X-ray detectors presents a critical challenge in macromolecular crystallography. We introduce the Jungfraujoch server, a revolutionary heterogeneous computing system that achieves unprecedented real-time data processing capabilities of 38 GB/s from a 9 Mpixel JUNGFRAU detector operating at 2 kHz. By synergizing FPGA-based data acquisition, GPU-optimized indexing, and CPU-driven compression, our system enables intelligent data reduction that maintains 100% of indexed frames while selectively preserving only 5% of non-indexed frames. This approach achieves a 96% true positive rate and 4% false negative rate in identifying non-indexed data, reducing data throughput from 38 GB/s to approximately 2 GB/s without compromising data quality. Field testing at the MicroMAX beamline at MAX IV demonstrates sustained performance at full frame rates, while successful deployments at multiple facilities showcase the versatility of the system beyond macromolecular crystallography and JUNGFRAU detectors. The Jungfraujoch server represents a significant advancement in scientific data management, offering a scalable solution for next-generation light sources and establishing a new paradigm for real-time data processing in experimental physics. Data collected with DCM: /data/visitors/micromax/20230350/20240521/raw/LysozymeJet8/LysozymeJet8-lysozyme1/LysozymeJet8-lysozyme1_1_master.h5 - Flux: ~10e12 ph/s Data collected with MLM: /data/visitors/micromax/20230350/20240521/raw/run2/LysozymeJet5/LysozymeJet5-lysozyme1/LysozymeJet5-lysozyme1_2_master.h5 - Flux: ~10e14 ph/s Thu, 01 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-48391-bd3808c1-6bf7-4229-8256-88ec4d2eb293 https://researchdata.se/en/catalogue/dataset/doi-10-48391-bd3808c1-6bf7-4229-8256-88ec4d2eb293 Lund University Jiaxin Duan, Hans-Christian Stadler, Jie Nan, Zdenek Matěj, Guillaume Gotthard, Florian Dworkowski, Monika Bjelčić, Oskar Aurelius, Aldo Mozzanica, Sascha Grimm, Max Burian, Thomas Ursby, Meitian Wang, Florian Leonarski This dataset contains data on yield, crop management, field measurements and field conditions for organic spring barley and winter wheat fields in agricultural areas of southern Sweden from 2020. The data was collected to determine yield limitations for these two crops and the relative importance and relationships between management (for example fertilisation, weeding, soil cultivation, preceding crop, crop rotation, time since conversion to organic farming) and its effects on various measurement of the field state (for example nutrient levels, weed, diseases) as well as soil characteristics and weather. The farms (56 in total) were all organically certified and located within the regions of Skåne, Halland and Västergötland, one field per farm and crop was visited. Field visits were done to take measurements in the field, these included plant nitrogen levels (through SPAD), weeds, pests and diseases measured during the growing season, and soil samples gathered in the end to analyse phosphorus and potassium levels, as well as pH and soil organic matter content. This data was gathered in four 2x2 meter observation plots in each field, and the data present averages across these four plots. After this the farmers were contacted to get information about the field's management operations and yield. Additional contextual data was gathered from landuse, weather and soil maps, averaged for the entire field and/or summed over time. Included in the dataset is both the original data and also values that have been regionally group mean centered, as used in the related publication. The data is split between spring barley (SB) and winter wheat (WW) and each row represents one field, 52 fields with spring barley and 29 with winter wheat. Fri, 19 Dec 2025 11:38:45 GMT https://researchdata.se/en/catalogue/dataset/2025-366 https://researchdata.se/en/catalogue/dataset/2025-366 Lund University Melanie Karlsson Rafaelle Reumaux Romain Carrié Ingrid Öborn Christine A. Watson Göran Bergkvist Sigrun Dahlin Johan Ekroos Johanna Wetterlind Henrik G. Smith Data comes from a cluster-randomized controlled trial where a locally adapted version of the school-based intervention Good Behavior Game was evaluated at elementary schools in Malmö, Sweden, during 2021-2022. All data is on a school or classroom-level. More details regarding the study are available in the associated study protocol. More detailed raw data and any related key codes are stored in a secure system used by Lund University (LUSEC) and is disposed of in the year 2032 at the earliest. Fri, 12 Dec 2025 16:28:00 GMT https://researchdata.se/en/catalogue/dataset/2025-128 https://researchdata.se/en/catalogue/dataset/2025-128 Lund University Dariush Djamnezhad Martin Bergström Björn Hofvander The file "Survey answers" is an Excel file that contains the data from a survey on attitudes towards different principles for responsibility distribution among the adult population of six Swedish municipalities 2020-2021. It contains 510 entries (i.e. answers from 510 respondents). The file "Variable info" is an Excel file that contains information about how to interpret the data from "Survey answers". Wed, 22 Oct 2025 08:51:47 GMT https://researchdata.se/en/catalogue/dataset/2025-254 https://researchdata.se/en/catalogue/dataset/2025-254 Lund University Erik Persson Kerstin Eriksson Åsa Knaggård This dataset contains bulk RNA-sequencing (RNA-seq) gene expression data from from 120 AML-samples from the subtypes NPM1 (n=33), AML-MR (n=30), TP53 (n=18), PML::RARA (n=8), CBFB::MYH11 (n=8), AML without class defining mutations (n=8), RUNX1::RUNX1T1 (n=3), KMT2A fusion genes (n=3), AML meeting the criteria for two subtypes (n=2), DEK-NUP214 (n=2), GATA2::MECOM (n=1), and bialleleic CEBPA mutation (n=1). The single cell libraries were constructed from bone marrow (n=102) or peripheral blood (n=18) using the TruSeq RNA Library Prep Kit v2 (Illumina) and sequenced on a NextSeq 500. Reads were aligned against human reference genome hg19 and read counts were determined using RSEM v1.2.30 (https://github.com/deweylab/RSEM) with gencode v19 as gene reference. Data is available as fpkm-values as determined by RSEM. Raw sequencing reads (fastq) are available at the European Genome-Phenome Archive (EGA) under accession ID EGAD50000001576: https://ega-archive.org/datasets/EGAD50000001576. Tue, 07 Oct 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-21557163 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-21557163 Lund University Henrik Lilljebjörn Thoas Fioretos