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Data for: Long-term body size change in multiple landbird species, long-term change in temperature and precipitation as well as associations between temperature, precipitation, and morphological change in multiple landbird species, 2004 – 2019, 2021 - 2022.
<p>Six data sets used to look for long-term change in precipitation and temperature, body size change and possible environmental drivers of morphological change in birds captured during spring or fall migration in and around Lackawanna State Park, northeastern Pennsylvania, USA.</p> <p>The file labeled daily_temp_precip.csv contains daily precipitation and average daily temperature data from the Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA) and the file called daily_temp_precip_1400 contains daily precipitation and daily temperature data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude.</p> <p>The file called band_data_final.csv contains data collected from the first capture of individuals of multiple species during spring or fall migration, the file called all_hy_env_morph.csv contains temperature and precipitation anomaly data from Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA), as well as morphological data from the first capture of all fall migrating young of the year.</p> <p>The file called all_hy_env_morph_1400.csv contains temperature and precipitation anomaly data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude as well as morphological data from the first capture of all fall migrating young of the year while the file called local_hy_env_morph.csv contains temperature and precipitation anomaly data as well as first capture of local young of the year.</p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
LongPMInd: long-term (1980-2022) daily ground particulate matter datasets in India
<p>The LongPMInd dataset, including daily PM2.5 and PM10 concentration (10km) for India during 1980-2022, is publicly accessible. All data are provided with NetCDF format with a spatial resolution of 10 km.<br><br></p>
Data, code, and outputs for the paper "Can Place-Based Crime Prevention Impacts be Sustained Over Long Durations? 11-Year Follow-Up of a Quasi-Experimental Evaluation of a CCTV Project"
<p>**********<br>Can Place-Based Crime Prevention Impacts be Sustained Over Long Durations? 11-Year Follow-Up of a Quasi-Experimental Evaluation of a CCTV Project<br>**********</p> <p>The phase-specific datasets for the main and displacement analyses are contained in the “1.datasets” folder. </p> <p>The scripts used to conduct the microsynthetic control matching analyses are contained in the “2.Rscripts” folder. The subfolders “3.outputs_main” and "4.outputs_displacement" contains all graphs and tables generated by the microsynth package. Please note that running the analysis will take several hours on most computer systems.</p> <p>The Microsoft Excel file displays the results of all models conducted for this analysis. Yellow tabs contain the WDD results presented in the text of the article. Red tabs are the per-capita (quarter-year) microsynth results used to calculate WDD values. All other tabs contain the raw microsynth outputs used to calculate the per-capita results. </p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Simulation data for benchmarking de novo long read transcriptome assembly software
<p>Method of simulation of differentially expressed biological replicates</p> <p>We first obtained a subset of transcripts that are widely expressed in the GTEx v9 dataset (92 samples) using Gencode comprehensive annotation (v44). We kept transcripts with more than 5 reads in at least 15 samples after Salmon quantification (18145 genes, 40509 transcripts), and stored their mean count per million (CPM) values as the control group’s baseline expression. We then generated a perturbed set of CPM values where transcript expression was changed by: (1) randomly selecting 1000 genes and changing all transcripts belonging to that gene concordantly (500 genes 2 fold up and 500 genes 2 fold down), (2) selected another 1000 genes randomly, and then select 2 random transcripts from the gene and swap their expression, (3) selected another 1000 genes randomly, and then select 1 random transcript to change its expression (500 transcripts 2 fold up and 500 transcripts 2 fold down). The updated CPM were stored as the perturbed group baseline expression. We then generated a count matrix and CPM matrix for 3 control replicates and 3 perturbed replicates with gamma distribution, followed by a Poisson distribution <a href="https://www.zotero.org/google-docs/?cUP4ui">(Baldoni et al., 2024)</a>. Both long-read and short-read FASTQ files were simulated using SQANTI-SIM with default settings and ONT R9.4 cDNA error profile (v 0.2.1) <a href="https://www.zotero.org/google-docs/?Qyopst">(Mestre-Tomás et al., 2023)</a>. The long read data contained 6 million reads in total, and an average read length of 1085 bp, and short read data was 100 bp paired-end. We then subsampled the short-read data to match the total number of base pairs in the long read data (6.5 billion bases). The simulated data was non-stranded, and contains 2000 DE genes, 2000 genes with DTU, 5927 transcripts with DTU and 6933 DE transcripts.</p> <p> </p>
Skogaryd data used for the paper: Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat Model.
<p>Dataset of abiotic and carbon exchange variables for Skogaryd drained afforested peatland. The dataset include measurements of soil temperature, ground water level, and carbon exhange as well as modelled carbon fluxes performed with the model ForSAFE-Peat </p>
Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
Polyadenylation landscape of in vivo long-term potentiation in the rat brain
<h3><strong>This repository contains data published along our corresponding manuscript and additional data resources generated/used throughout this work. <br>______________________________________________________________________________</strong></h3> <h2><strong>Source data accompanying the manuscript:<br></strong></h2> <p><strong>Supplementary Table 1. Key resource table. (A) </strong>Characteristics of analyzed material (e.g. sample identifiers, number of animals used, reads produced, accession numbers). <strong>(B)</strong> Key resources (antibodies, reagents, software).<strong><br><br>Supplementary Table 2. Summary of dentate gyri DRS data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 3. Summary of dentate gyri cDNA data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 4.</strong> <strong>High-confidence PASs.</strong> <strong>(A)</strong> PASs predicted for datasets obtained 10 min after LTP induction by TAPAS. <strong>(B)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 10 min after LTP induction. <strong>(C)</strong> PASs predicted for datasets obtained 60 min after LTP induction. <strong>(D)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 60 min after LTP induction.</p> <p><strong>Supplementary Table 5.</strong> <strong>Nonadenosine profiling upon LTP induction. (A)</strong> Summary of Ninetails pipeline for dentate gyrus. <strong>(B)</strong> List of genes containing semi-templated poly(A) tails with their adjacent nucleotide contexts.</p> <p><strong>Supplementary Table 6. Summary of synaptoneurosomal DRS/cDNA data per gene.</strong> <strong>(A)</strong> Differential expression and differential adenylation data for unfractionated synaptoneurosomes DRS sequencing. (B) <strong> </strong>Summary of Ninetails pipeline for unfractionated synaptoneurosomes DRS sequencing. (C) Differential expression and differential adenylation data for monoribosome-bound mRNA synaptoneurosomes cDNA sequencing. (D) Differential expression and differential adenylation data for polyribosome-bound mRNA synaptoneurosomes cDNA sequencing. (E) Differential expression and differential adenylation data for unfractionated synaptoneurosomes cDNA sequencing.<br><br><strong>Supplementary Information</strong> - supplementary figures and captions.<br><br><strong>______________________________________________________________________________</strong></p> <h2><strong>Additional data resources:</strong></h2> <p><strong>CPEB1_motif.meme </strong>- CPE1 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<br><strong><br>CPEB2_4_motif.meme</strong> - CPE2,4 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<strong><br></strong></p> <p><strong>mRatBN7.2_TAPAS_ref_flat.txt</strong> - mRatBN7.2 reference annotation in format required by TAPAS<br><br><strong>mRatBN7.2_LAPA.gtf </strong>- mRatBN7.2 reference annotation in format required by LAPA</p> <p><strong>FIMO_output.zip</strong> - compressed folder with motif predictions provided by FIMO software.<strong><br><br>LAPA_output_dentate_gyrus.zip </strong>- compressed folder with poly(A) clusters predicted by LAPA software. Each timepoint is represented by separate output. <br><strong><br>TAPAS_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by TAPAS software. Each timepoint is represented by separate output. <strong><br><br>Ninetails_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from dentate gyrus. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<br><strong><br>Ninetails_output_synaptoneurosomes.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from synaptoneurosomes. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<strong><br><br>PolyA_clusters_high_confident.bed </strong>- High-confidence poly(A) clusters annotation in bed format.<strong><br></strong></p>
ChinaWheat30L: Long-term winter wheat maps of China at 30-m resolution from 2000 to 2023
<p>This is the long-term winter wheat map product of China at 30-m resolution from 2000 to 2023 (ChinaWheat30L). The product was generated using a knowledge-guided machine learning approach and the integration of satellite remote sensing and environmental datasets.</p>
Long-Lived Ensembles of Shallow NV− Centers in Flat and Nanostructured Diamonds by Photoconversion
<p>Shallow, negatively charged nitrogen-vacancy centers (NV−) in diamond have been proposed for high-sensitivity magnetometry and spin-polarization transfer applications. However, surface effects tend to favor and stabilize the less useful neutral form, the NV0 centers. Here, we report the effects of green laser irradiation on ensembles of nanometer-shallow NV centers in flat and nanostructured diamond surfaces as a function of laser power in a range not previously explored (up to 150 mW/μm2). Fluorescence spectroscopy, optically detected magnetic resonance (ODMR), and charge-photoconversion detection are applied to characterize the properties and dynamics of NV− and NV0 centers. We demonstrate that high laser power strongly promotes photoconversion of NV0 to NV− centers. Surprisingly, the excess NV− population is stable over a timescale of 100 ms after switching off the laser, resulting in long-lived enrichment of shallow NV−. The beneficial effect of photoconversion is less marked in nanostructured samples. Our results are important to inform the design of samples and experimental procedures for applications relying on ensembles of shallow NV− centers in diamond.</p>
Long term irrigation experiment
<p>Climate change exposes ecosystems to strong and rapid changes in their environmental boundary conditions mainly due to the altered temperature and precipitation patterns. It is still poorly understood how fast interlinked ecosystem processes respond to altered environmental conditions, if these responses occur gradually or suddenly when thresholds are exceeded, and if the patterns of the responses will reach a stable state. We conducted an irrigation experiment in the Pfynwald, Switzerland from 2003-2018. A naturally dry Scots pine (<i>Pinus sylvestris </i>L.) forest was irrigated with amounts that doubled natural precipitation, thus releasing the forest stand from water limitation. The aim of this study was to provide a quantitative understanding on how different traits and functions of individual trees and the whole ecosystem responded to increased water availability, and how the patterns and magnitudes of these responses developed over time. We found that the response magnitude, the temporal trajectory of responses, and the length of initial lag period prior to significant response largely varied across traits. We detected rapid and stronger responses from above-ground tree traits (e.g., tree-ring width, needle length, and crown transparency) compared to below-ground tree traits (e.g., fine root biomass). The altered above-ground traits during the initial years of irrigation increased the water demand and trees adjusted by increasing root biomass during the later years of irrigation, resulting in an increased survival rate of Scots pine trees in irrigated plots. The irrigation also stimulated ecosystem-level foliar decomposition rate, fungal fruit body biomass, and regeneration abundances of broadleaved tree species. However, irrigation did not promote the regeneration of Scots pine trees which are reported to be vulnerable to extreme droughts. Our results provide extensive evidence that tree- and ecosystem-level responses were pervasive across a number of traits on long-term temporal scales. However, after reaching a peak, the magnitude of these responses either decreased or reached a new stable state, providing important insights into how resource alterations could change the system functioning and its boundary conditions.</p>
Fig. 2 in The identity of the long-overlooked Ronabea morindoides and Patabea tenuiflora, synonymous with a species of Appunia (Rubiaceae)
Fig. 2. – Holotype of Patabea tenuiflora DC. A. Detail of flower buds with abaxial corniform appendages. [G–DC: G00667064]
Fig. 3 in The identity of the long-overlooked Ronabea morindoides and Patabea tenuiflora, synonymous with a species of Appunia (Rubiaceae)
Fig. 3. – Appunia morindoides (A. Rich.) Delprete, C.M. Taylor & T. McDowell. A. Branchlet with inflorescence; B. Stem closeup, showing distalmost nodes with stipules; C. Inflorescence with flower buds, being visited by small ants; D. Inflorescence with most corollas fallen off, showing calyx limbs and disks; E. Open flower (note corolla lobes with apical appendices, both galeate adaxial appendages and abaxial corniform appendages); F. Infructescence with ripe fruits. [A–C, E: trail to Savane-Roche Corail, Kourou, French Guiana; D: trail Grand Boeuf Mort, Saül, French Guiana; F: Montagne des Singes, French Guiana] [Photos: Hervé Galliffet]
Long-term litter fall data series from 34 boreal forest stands in Finland
<p><strong>Introduction</strong></p> <p>Litter fall data were collected on a network of 34 forest sampling plots in Finland from late 1950s to 2010s. The data collection spanned different time periods in different sampling plots. The data have been used for studies on the flowering and seed crop of forest trees, air quality, and insect damage (see list of publications in the end of this document). They have been used to develop seed production and needle litter fall models for Scots pine (<em>Pinus sylvestris</em>) and Norway spruce (<em>Picea abies</em>), a branch litter model for pine, and total litter fall models used in greenhouse gas inventories.</p> <p><strong>Data collection</strong></p> <p>The litter fall collection was set up in mature, single species stands. The focal tree species include Scots pine (<em>Pinus sylvestris</em>), Norway spruce (<em>Picea abies</em>), Silver birch (<em>Betula pendula</em>), Downy birch (<em>Betula pubescens</em>), Grey alder (<em>Alnus</em> <em>incana</em>), European rowan (<em>Sorbus aucuparia</em>), European larch (<em>Larix decidua</em>), and Siberian larch (<em>Larix sibirica</em>). The sampling plots varied in shape and in size with a typical area of 0.1-0.25 ha. Between 6 and 30 litter collecting funnels were used per plot. The funnels were made of galvanized sheet metal and attached to cloth bags to collect the falling litter. The sampling sites were monitored for changes in conditions, such as natural disturbances, tree harvesting, forestry operations, or construction on the plot or in its immediate proximity (none observed).</p> <p>The litter samples were collected from the sampling plots, usually four to six times per year in spring to autumn. The samples were dried in room temperature (except male flowers in 1960s – 1970s, see note in Table 1) and stored in paper bags. The dried samples were sorted into litter fractions (Table 1). These fractions varied between tree species and between years to some extent. Cones and seeds were counted, and all other litter fractions were weighed to the nearest milligram.</p> <table> <caption>Table 1. Litter fractions and their codes. The code of the litter fraction is used in the data files.</caption> <thead> <tr> <th scope="col">Code</th> <th scope="col">Litter fraction</th> <th scope="col">Description / note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Male flowers</td> <td>In the 1960s – 1970s male flowers were dried in 105°C for 24 hours (Sarvas 1962, 1968).</td> </tr> <tr> <td>2</td> <td>Seeds</td> <td>Seed wings and seeds from species other than the focal one were included into the “other litter” fraction.</td> </tr> <tr> <td>3</td> <td>Female flowers</td> <td> </td> </tr> <tr> <td>4</td> <td>Needles</td> <td> </td> </tr> <tr> <td>5</td> <td>Insects and their faeces</td> <td> </td> </tr> <tr> <td>6</td> <td>Other litter</td> <td> </td> </tr> <tr> <td>7</td> <td>Lichens, branches, and tree bark</td> <td>In some years lichens, branches, and bark were combined in the same fraction, and in some they were separated in their own fractions (numbers 12-14 below).</td> </tr> <tr> <td>8</td> <td>Small cones (1 year)</td> <td>For pine stands, cones were separated into one-year old small cones and large cones. Cones were counted.</td> </tr> <tr> <td>9</td> <td>Cones</td> <td> </td> </tr> <tr> <td>10</td> <td>Cones and loose scales</td> <td>In some years loose scales were included in the same fraction as cones, and in some they were included into the “other litter” fraction.</td> </tr> <tr> <td>11</td> <td>Shifting dust</td> <td> </td> </tr> <tr> <td>12</td> <td>Branches</td> <td> </td> </tr> <tr> <td>13</td> <td>Lichens</td> <td> </td> </tr> <tr> <td>14</td> <td>Tree bark</td> <td> </td> </tr> <tr> <td>15</td> <td>Leaves</td> <td>For deciduous stands, leaves were separated into small (diameter < 1 cm) and large leaves (diameter > 1 cm), while for conifer stands all leaves were included into the “other litter” fraction.</td> </tr> <tr> <td>16</td> <td>Berries</td> <td> </td> </tr> </tbody> </table> <p>In addition to litter fall data, tree stand data were collected on most of the plots in some years. In the tree stand inventories, all trees in the sampling plot with diameter at breast height ≥ 7 cm were mapped, and all trees were counted and measured for diameter (at breast height and at 6 meters), total height, and height to first living branches. Stand basal area and dominant diameter and height were calculated. Stand age was estimated based on core samples from five trees outside but representative of the sample plot. Crown coverage was estimated with a Cajanus tube.</p> <p><strong>Description of the data files</strong></p> <p>The litter fall data is in eight csv-files, one per tree species. The files are named “Litter_Tree_species.csv”, for example “Litter_Betula_pendula.csv”.</p> <p>Variables (in columns) are consistent across the files (explained in Table 2), but note that there are varying numbers of columns between the tables in the files, as each litter collecting funnel has its own column and different maximum numbers of funnels were used in different sampling sites and tree species (see row “S1 – S30” in Table 2 for more details).</p> <table> <caption>Table 2. Variables included in the litter fall data files.</caption> <thead> <tr> <th scope="col">Variable name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Form</td> <td>Number identifying the original paper form.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Tree species code: 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch, 5 = Grey alder, 6 = Siberian larch, 7 = European larch, 8 = European rowan.</td> </tr> <tr> <td>LitterFraction</td> <td>Code for the litter fraction (1-16), explained in Table 1.</td> </tr> <tr> <td>Coefficient</td> <td>Coefficient used to transform the weight of the litter (g) to weight per square meter (g m<sup>-2</sup>). The coefficient is based on the number and area of the collection funnels.</td> </tr> <tr> <td>Date</td> <td>Date of sample collection.</td> </tr> <tr> <td>Period</td> <td>Variable used to define the time of data collection as calendar year or phenological year. The variable is based on the schedule of data collection in different years and on the focal tree species so that it corresponds to the species-specific litter fall schedule. For spruce, the peak needle fall is in the spring, so the calendar year is appropriate for describing the temporal variation in litter fall. For pine, the peak needle fall is in August–September, so a phenological year defined as July 1<sup>st</sup> – June 30<sup>th</sup> is appropriate for describing the temporal variation in litter fall. Period = -1 means the values in the row are allocated to the previous calendar year; period = 0 means the values are allocated to the current calendar year; and period = 1 means the values are allocated to the next calendar year.</td> </tr> <tr> <td>S1 – S30</td> <td>Columns S1 – S30 refer to litter collection funnels 1 – 30. The maximum number of funnels varies between tree species: for <em>silver birch</em> up to 30 funnels were used per plot, for downy birch up to 20 funnels, for spruce up to 10 funnels, for pine up to 15 funnels, for rowan 10 funnels, and for both larch species and alder 8 funnels. Missing values (NA) mean that the funnel was not used in the plot. Value -1 means that the funnel was used but the sample was missing. The values give the dry weight of the litter in mg.</td> </tr> <tr> <td>Combined</td> <td>For some samples, litter collected by different funnels has been combined and the total weight is shown in column S1. In this column, 0 = values have not been combined, and 1 = values have been combined.</td> </tr> <tr> <td>TotalWeight</td> <td>Total weight of the litter (mg).</td> </tr> <tr> <td>TotalWeightArea</td> <td>Total weight of the litter per area (mg m<sup>-2</sup>). Value is same as TotalWeight × Coefficient.</td> </tr> <tr> <td>Note</td> <td>Note</td> </tr> </tbody> </table> <p>Tree stand data is in one csv-file, named “Tree_stand_data.csv”, and can be combined with the litter fall data based on the sample plot abbreviations (variable “PlotAbbr” in both litter fall data files and the tree stand data file). Note that there is no tree stand data available for all the same years as litter fall data. There is no tree stand data available at all for one downy birch site (abbreviation HEI568), one spruce site (NOO85), one pine site (HEI566), and the alder, rowan, and larch sites. Tree stand data variables are explained in Table 3.</p> <table> <caption>Table 3. Variables included in the tree stand data file.</caption> <tbody> <tr> <td>Variable name</td> <td>Description</td> </tr> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Dominant tree species. 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch.</td> </tr> <tr> <td>Age</td> <td>Stand age (years).</td> </tr> <tr> <td>SiteType</td> <td>Forest site type describing site productivity. 2 = xeric heath forest, 3 = sub-xeric heath forest, 4 = mesic heath forest, 5 = herb-rich heath forest. </td> </tr> <tr> <td>North</td> <td>North coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>East</td> <td>East coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>Elevation</td> <td>Elevation (m above sea level).</td> </tr> <tr> <td>N</td> <td>Stem number (ha<sup>-1</sup>).</td> </tr> <tr> <td>BA</td> <td>Basal area (m<sup>2</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>DomD</td> <td>Diameter (cm) of dominant trees.</td> </tr> <tr> <td>DomH</td> <td>Height (m) of dominant trees.</td> </tr> <tr> <td>CrownLength</td> <td>Crown length (m).</td> </tr> <tr> <td>V</td> <td>Stem volume (m<sup>3</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>CrownCover</td> <td>Crown coverage (%).</td> </tr> </tbody> </table> <p> </p> <p><strong>List of publications </strong></p> <p>Hilli, A., Hokkanen, T., Hyvönen, J. & Sutinen, M.-L. 2008. Long-term variation in Scots pine seed crop size and quality in northern Finland. Scandinavian Journal of Forest Research 23(5): 395-403. </p> <p>Koski, V. & Tallqvist, R. (1978). Results of long-time measurements of the quantity of flowering and seed crop of forest trees (in Finnish with English summary). Folia Forestalia, 364, 1-60. </p> <p>Kouki, J. & Hokkanen, T. 1992. Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors. Oecologia 89: 176-181. </p> <p>Lehtonen, A., Lindholm, M., Hokkanen, T., Salminen, H. & Jalkanen, R. 2008. Testing dependence between growth and needle litterfall in Scots pine - a case study in northern Finland. Tree Physiology 28(11): 1741-1749. </p> <p>Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K.T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling 180(2-3): 305-315. </p> <p>Leikola, M., Raulo, J. & Pukkala, T. (1982). Prediction of the variations of the seed crop of Scots pine and Norway spruce (in Finnish with English summary). Folia Forestalia, 537, 1-43. </p> <p>Niemistö, P., Hokkanen T. & Varama, M. 2004. Karikemäärän muutokset 1982–2001 ja puiden kunto lumi- ja hallamittariesiintymän vaivaamissa koivikoissa Noormarkussa. Metsätieteen aikakauskirja 1/2004: 21–41. </p> <p>Poikolainen, J. & Kuusinen, M. 2000. Abundance of epiphytic lichens in litterfall during 1967-1994. In: Forest condition in a changing environment - the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers / Ed. Mälkönen, E. Sivut: 171-172. </p> <p>Pukkala, T. 1987a. A model for predicting the seed crop of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 135-144. </p> <p>Pukkala, T. 1987b. Effect of seed production on the annual growth of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 145-158. </p> <p>Pukkala, T., Hokkanen, T. & Nikkanen, T. 2010. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fennica 44(4): 629-642. </p> <p>Ranta, E., Lindström, J., Kaitala, V., Crone, E., Lundberg, P., Hokkanen, T. & Kubin, E. 2010. Life history mediated responses to weather, phenology and large-scale population patterns. In: Hudson, I. L & Keatley, M. R. (eds.). Phenological Research. Springer, Dordrecht Heidelberg London New York, Netherlands. p. 321-338. </p> <p>Raulo, J. & Hokkanen, T. 1989. Litter fall of Alnus incana and Alnus glutinosa (in Finnish with English summary). Folia Forestalia 738. 25 s.</p> <p>Saarsalmi, A., Starr, M., Hokkanen, T., Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. 2007. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 242(2-3): 578-586. </p> <p>Sarvas, R. (1962). Investigations on the flowering and seed crop of Pinus Silvestris. Communicationes Instituti Forestalis Fenniae, 53, 1-198. </p> <p>Sarvas, R. 1968. Investigation on the flowering and seed crop of Picea abies. Communicationes Instituti Forestalis Fenniae 67.5. 84 pp. </p> <p>Starr, M., Saarsalmi, A., Hokkanen, T., Merilä, P. & Helmisaari, H.-S. 2005. Models of litterfall production for Scots pine (Pinus sylvestris L.) in Finland using stand, site and climate factors. Forest Ecology and Management 205: 215-225. </p> <p>Ťupek, B., Mäkipää, R., Heikkinen J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M. & Lehtonen, A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Environment Research 20: 283–304</p>
Long reads training material for 'Quality Control' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for reads Quality Control.</p> <p>PacBio HiFi reads were provided by PacBio - GIAB sample HG002 (https://www.pacb.com/smrt-science/smrt-resources/datasets/) and was downsampled using seqtk (https://github.com/lh3/seqtk)</p> <p>Nanopore reads were provided by Tim Kahlke as part of "Long-Read, long reach Bioinformatics Tutorials" (https://timkahlke.github.io/LongRead_tutorials/) and was basecalled using Guppy v5.0.2 (dna_r9.4.1_450bps_sup.cfg).</p>
Fig. 1 in A Long-Term Comparison Of Laying Date And Clutch Size In The Red-Backed Shrike (Lanius Collurio) In Silesia, Southern Poland
Fig. 1. The number of Red-backed Shrike clutches laid during successive five-day periods (1 = 6–10 May) in the two study periods. For sample size – see text
Fig. 10 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 10. Trends in the relative abundance of trophic guilds in Lake Fenéki (Piscivores: y = 0.06 + 0.003x; R2 = 0.757; P> 0.00001)
Fig. 9 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 9. Proportion of each species in the cumulative abundance of non-native fish species in Lake Fenéki
Fig. 7 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 7. PCA biplot of the arcsin-square root transformed relative abundance data of the whole sampling period (1992–2011) (Variables: Sampling years; Objects: Relative abundances) (abbreviations were constructed from the Latin names of the species, using the first 3 characters of genus and species
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