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ICOS ATC/CAL Flask Release from Norunda (100.0 m)
No full textcan contain atmospheric composition data for any of a number of gases; the data can be a mole fraction, isotope ratio, or other related quantity
Lehner, I., Molder, M. (2025). ICOS ATC/CAL Flask Release from Norunda (100.0 m), 2019-11-12–2025-03-25, ICOS RI, https://hdl.handle.net/11676/ulYMXNB36KyjKLlukLdzGCW
ICOS ATC Meteo Release from Hyltemossa (150.0 m)
No full textQuality-controlled meteo data
Heliasz, M., Biermann, T. (2025). ICOS ATC Meteo Release from Hyltemossa (150.0 m), 2017-09-26–2025-03-31, ICOS RI, https://hdl.handle.net/11676/XtTX24u5eGCJmZYq3dBBYKj
ICOS ATC Meteo Release from Hyltemossa (30.0 m)
No full textQuality-controlled meteo data
Heliasz, M., Biermann, T. (2025). ICOS ATC Meteo Release from Hyltemossa (30.0 m), 2017-09-26–2025-03-31, ICOS RI, https://hdl.handle.net/11676/zs6w7ImEgOejmmk7VL3pHje
10X single cell sequencing of HNT34 cocultured with Tcells with or without antibody targeting SLAMF6
No full textThis dataset contains fastq-files from single cell 5' RNA sequencing of the AML cell line HNT34 and normal T cells following co-culture with and without an antibody blocking SLAMF6 (TNC-1). The libraries were prepared using 10X GEM-X Universal 5' Gene Expression v3 Reagent Kit. In total, the dataset contains sequenced gene expression libraries from four samples (HNT34 co-cultured with T cells from two different donors; for both donors there is one sample with and one sample without the blocking antibody).
This dataset is 1 of 1 included in the study titled Aberrant expression of SLAMF6 constitutes a targetable immune escape mechanism in acute myeloid leukemia, http://identifiers.org/ega.study:EGAS50000001085
Mate pair whole genome sequencing of 98 AML samples
No full textThis dataset contains bam-files from Mate-pair whole genome sequencing of 98 AML samples. DNA was extracted from either bone marow or peripheral blood from primary AML samples. The libraries were prepared using Illumina Nextera mate pair library preparation kit, generating long-insert (2-8 kb) paired end libraries. These were sequenced on an Illumina NextSeq 500 using 2x76bp paired end chemistry. The fastq files generated by sequencing were aligned to the human hg19 reference genome (ucsc.hg19.fasta from the GATK resource bundle) using bwa (0.7.15-r1140) and duplicate reads were identified using samblaster (0.1.24).
This dataset is 1 of 4 included in the study titled The cellular state space of AML unveils novel NPM1 subtypes with distinct clinical outcomes and immune evasion properties, http://identifiers.org/ega.study:EGAS50000001084
Physical variables - lake temperature profile from Erssjön, Floating platform
No full textHigh frequency temperature profiles are measured in lakes of SITES Water to be able to determine the thermal structure.
Skogaryd Research Catchment (2025). Physical variables - lake temperature profile from Erssjön, Floating platform, 2019-01-01–2019-12-31 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/jseSeXW4nzv_Y6M7GUkDdZJ
PLECHAR – Morphological and anatomical character information for 661 pleurocarpous mosses for use in evolutionary and ecological studies
No full textThe data file PLECHAR.xlsx (Excel) includes four sheets
• SPECIES AND CHARACTER STATES, with the data.
• STUDIED SPECIMENS, specifying which specimens were studied.
• CITED LITERATURE, listing the literature cited in the PLECHAR.xlsx file itself.
• CHARACTER EXPLANATIONS – READ!, with information about characters that are not self-explanatory.
The data (SPECIES AND CHARACTER STATES) includes morphological and anatomical character information for 661 pleurocarpous moss taxa, mostly at the species level, described under 106 headings. I compiled the morphological and anatomical character data for pleurocarpous mosses mainly from studies where I scored the character information myself. Information about which studies this is based on is included in the README file. A few additions were made from the literature, as indicated in the sheet CITED LITERATURE in the data file. Since I scored almost all information myself, any observational bias should be approximately the same throughout the data and the information on a particular character should be comparable between taxa.Datafilen PLECHAR.xlsx (Excel) innehåller fyra blad
• SPECIES AND CHARACTER STATES, med själva data.
• STUDIED SPECIMENS, en lista med de kollekter som studerats.
• CITED LITERATURE, en lista med litteratur som citeras i filen PLECHAR.xlsx.
• CHARACTER EXPLANATIONS – READ!, med information om karaktärer som kan vara svåra att förstå.
Själva data (SPECIES AND CHARACTER STATES) består av morfologisk och anatomisk information för 661 pleurokarpa bladmossor, oftast på artnivå, beskrivet under 106 rubriker. Jag har samlat den morfologiska och anatomiska informationen för pleurokarpa bladmossor i huvudsak från studier där jag själv har observerat karaktärerna. Information om vilka dessa studier är finns i README-filen. Ett fåtal kompletteringar ur litteraturen finns med bland data, och litteraturkällorna till dessa finns i bladet CITED LITERATURE i datafilen. Eftersom jag gjort nästan alla observationer själv bör förekommande observationsbias vara ungefär den samma i hela datasetet, varför information om en specifik karaktär bör vara jämförbar mellan arter
Chemical variables - stream from Röbäcksdalen Catchment, Sampling point 4
No full textManual grab samples from the stream for chemical analysis are taken on a biweekly basis during ice-free conditions, and on a monthly basis in the presence of stream ice.
Röbäcksdalen Field Research Station (2025). Chemical variables - stream from Röbäcksdalen Catchment, Sampling point 4, 2016-06-30–2025-05-27 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/YtBPe8T6XTDrDGJOko9VRmW
Physicochemical and biological variables measured in 110 national monitoring lakes between 1992 and 2022.
No full textPhysicochemical and biological data 110 national monitoring lakes for the years 1992 to 2022 were downloaded from the data host https://miljodata.slu.se/MVM/. Climate data were extracted from Climate Research Unit gridded Time Series dataset (version 4.06; Harris et al., 2020; https://crudata.uea.ac.uk/cru/data/hrg/)
Eight data sets were compiled:
1. Physicochemical variables from surface water samples (10 variables): EU id Year Colour_absorbance_420nm Ca mg/l Conductivity_mS/m pH TOC mg/l C DIN_ µg/l_N TP_ µg/l_P Water_Temperature_°C. Lake physicochemical and climate data were log10(x + 1) transformed to approximate normal distribution prior to the analyses.
2. Climate data: annual mean air temperature (°C) and precipitation (mm per month for each lake in each year) extracted from the interpolated Climate Research Unit gridded Time Series dataset (version 4.06; Harris et al., 2020; https://crudata.uea.ac.uk/cru/data/hrg/)
3. Phytoplankton taxon abundances (biovolumes) for individual taxa collected in August (n = 780 taxa). Biovolumes were Hellinger transformed.
4. Phytoplankton metrics (n=5): number of taxa, taxon biovolumes, diversity (Hills N2), % cyanobacteria and between-year Euclidean distance*.
5. Littoral taxon abundances (numbers per unit effort) for samples collected in October/November (n=681 taxa). Abundances were Hellinger transformed.
6. Littoral metrics (n=4): number of taxa, total abundance, diversity (N2) and between-year Euclidean distance
7. Profundal taxa abundances (individual per m2) for samples collected in October/November (n=321 taxa). Abundances were Hellinger transformed.
8. Profundal metrics (n=4): number of taxa, total abundance, diversity (N2) and between-year Euclidean distance.
* Between-year Euclidean distances were calculated using detrended correspondence analysis axes 1-3 as as d=√((x2 – x1)² + (y2 – y1)²) + (z2 – z1)²), where x1, y1, z1 represent DC axis scores one, two and three, respectively, at any year and x2, y2, z2 represent DC axis scores one, two and three, respectively, for the previous year
Supplementary Data for "GPSeq maps the radial organization of eukaryotic genomes along the nuclear periphery-center axis"
No full textWe offer a nextflow pipeline (https://github.com/BiCroLab/nextflow-radiantkit) based on Radiankit (https://github.com/BiCroLab/radiantkit) to perform image analysis on YFISH images obtained from Genomics loci Positioning by Sequencing (GPSeq) experiment. This help researchers assess the radial digestion of the chromatin by restriction enzyme as indicated in GPSeq protocol.
Here we provide sample data to perform the test run for the nextflow-radiantkit pipeline.
We include an example report generated by the pipeline from a GPSeq experiment performed on hTERT-immortalized retinal pigment epithelial cells (hTERT RPE-1) cell line for a doubling time point series (30 sec, 1 min, 2 min, 4 min, 8 min, 16 min, 32 min, 64 min).
Note: this dataset does not reproduce the radiantkit report as in the Supplementary Data included in this repository. It only help users of the nextflow-radiantkit to ensure all the requirements to run the pipeline are met and the pipeline can run through all processes.
We provide a nextflow pipeline (https://github.com/BiCroLab/nextflow-gpseq) to enable users to analyse GPSeq sequencing data without high level of bioinformatics expertise.
Here we include an example report of generated using MultiQC by the nextflow pipeline from a GPSeq experiment performed on hTERT-RPE-1 cell line for a doubling time point series (30 sec, 1 min, 2 min, 4 min, 8 min, 16 min, 32 min, 64 min).
Raw sequencing data is available on European Nucleotide Archive (accession: PRJEB89772)