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957 results for “conferences”

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zenodo36/100

REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-3. Bimanual wire and cable manipulation. Data related to a paper for the conference SysInt 2022

The datasets contain data recorded during the experiment reported in the paper: G. Laudante, and S. Pirozzi, “An Intelligent System for Human Intent and Environment Detection Through Tactile Data,” 6th International Conference on System-Integrated Intelligence (SysInt 2022), Genova, Italy. (DOI: 10.1007/978-3-031-16281-7_47)

opencc-by-4.0Dec 2021View details →

dryad36/100

Fitness costs associated with a GABA receptor mutation conferring dieldrin resistance in Aedes albopictus

Understanding the dynamics of insecticide resistance genes in mosquito populations is pivotal for a sustainable use of insecticides. Dieldrin resistance in Aedes albopictus is conferred by the alanine to serine substitution (A302S or RdlR allele) in the γ-aminobutyric acid (GABA) receptor encoded by the Rdl gene. On Reunion Island, dieldrin resistance was initially reported in natural Ae. albopictus populations sampled in 2008 despite the ban of dieldrin since 1994. To monitor insecticide resistance in Ae. albopictus on the island and to identify its drivers, we measured (i) the frequency of resistance alleles in 19 distinct natural populations collected between 2016 and 2017, (ii) fitness costs associated with dieldrin resistance in laboratory-controlled experiments, and (iii) the resistance conferred by RdlR to fipronil, an insecticide widely used on the island and reported to cross-react with RdlR. The results show a persistence of RdlR in Ae. albopictus natural populations at low frequencies. Among the measured life history traits, mortality in pre-imaginal stages, adults' survival as well as the proportion of egg-laying females were significantly affected in resistant mosquitoes. Finally, bioassays revealed resistance of RdlR mosquitoes to fipronil, suggesting that the use of fipronil in natura could select for the RdlR allele. This study shows that dieldrin resistance is persistent in natural mosquito populations likely as a result of combined effects between fitness costs associated with RdlR and selection exerted by cross-reacting environmental insecticides such as fipronil.

opencc-zeroOct 2022View details →

zenodo36/100

Activity at the DAEMON booth in the European Conference on Networks and Communications

@h2020daemon booth and 3 Demos at European Conference on Networks and Communications (@EuCNC) 2022 @Telefonica_En @tudelft @InformaticaUMA @IMDEA_SOFTWARE @UC3M @nec_sws @i2CAT @ADLINK_Tech @IMEC @BellLabs @SrsSystems @wings_ict @zettascaletech <a href="https://www.youtube.com/hashtag/h2020daemon">#h2020daemon</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a> @EU_H2020 @5GPPP

opencc-by-4.0Oct 2022View details →

zenodo36/100

Overview of the DAEMON booth at the European Conference on Networks and Communications

opencc-by-4.0Oct 2022View details →

zenodo36/100

Complete telomere-to-telomere genomes uncover virulence evolution conferred by chromosome fusion in oomycete plant pathogens

Variations in chromosome number are occasionally observed among oomycetes, a group that includes many plant pathogens, but the emergence of such variations and their effects on genome and virulence evolution remain ambiguous. We generated complete telomere-to-telomere genome assemblies for Phytophthora sojae, Globisporangium ultimum, Pythium oligandrum, and G. spinosum. Reconstructing the karyotype of the most recent common ancestor in Peronosporales revealed that frequent chromosome fusion and fission drove changes in chromosome number. Centromeres enriched with Copia-like transposons may contribute to chromosome fusion and fission events. Chromosome fusion facilitated the emergence of pathogenicity genes and their adaptive evolution. Effectors tended to duplicate in the sub-telomere regions of fused chromosomes, which exhibited evolutionary features distinct to the non-fused chromosomes. By integrating ancestral genomic dynamics and structural predictions, we have identified secreted Ankyrin repeat-containing proteins (ANKs) as a novel class of effectors in P. sojae. Phylogenetic analysis and experiments further revealed that ANK is a specifically expanded effector family in oomycetes. These results revealed chromosome dynamics in oomycete plant pathogens, and provided novel insights into karyotype and effector evolution.

opencc-by-4.0Apr 2024View details →

zenodo36/100

The intracellular C-terminus confers compartment-specific targeting of voltage-gated calcium channels

This table contains all tabulated data for: Chin and Kaeser, 2024. "The intracellular C-terminus confers compartment-specific targeting of voltage-gated calcium channels." Detailed methods are provided in the paper.

opencc-by-4.0Jun 2024View details →

zenodo36/100

Wind Value: Second Conference 2024 Situation in Ireland Jim Hughes Video

Video 17mins 33 seconds, of the presentation by Jim Hughes on the topic " The Ageing Portfolio of Wind Farms in Ireland - Key Issues". The presentation considers the existing fleet of wind farms in Ireland and suggests ways that may encourage more of these to stay in production after the end of their planning permission period. This took place at the second Wind Value conference on 29th May 2024 in the Ellen Hutchins Building, of the Environmental Research Institute of University College Cork, Ireland.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Aisa 1st annual conference "Nostra res agitur: open science as a social question" [section 2]

Video degli interventi del pomeriggio del 22 ottobre 2015, secondo l'ordine seguente: saluti delle autorità (0:0:00-0:03:06); saluti del Presidente (0:03:07-0:12:52); J-C. Guédon (0:12.53-1:14:22); G. Destro Bisol (1:14:23-1:45:10); dibattito (1:45:11-1:56:04). La registrazione è stata curata dal Team Elearning ed Eventi del Centro Serra dell'università di Pisa.

opencc-by-4.0Oct 2015View details →

zenodo36/100

Wind Value: First Conference 2022, Circular Economy for Wind, Anne Velenturf, Video

Video of 34 Minds and 54 seconds, on A Sustainable Circular Economy for Wind: The Bigger Picture, from Anne Velenturf of the Univesity of Leeds. Anne is a leading scholar in the circular economy.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Wind Value: First Conference 2022, Blade Bridge Concept to Reality, Kieran Ruane, Video

Video of 33 mins 1 second on The Blade Bridge - From Concept to Reality, by Kieran Ruane. Describing the design of the world's second bridge made from used wind turbine blades, installed on the Youghal Midleton Greenway, Co Cork, Ireland

opencc-by-4.0Jul 2024View details →

zenodo36/100

Wind Value: First Conference 2022, Research Opportunities for Wind Energy, Dave Linehan, Video

VIdeo of 9 mins and 35 seconds, on the Research Opportunities for the Wind Energy Sector, by Dave Linehan of Wind Energy Ireland.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Wind Value: First Conference 2022, Wind Blade Repurposing, Lawrence Bank, Video

Video of 32 mins and 7 seconds, dealing with the repurposing of used wind turbine blades, by Lawrence (Larry) Bank of Georgia Tech.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Wind Value: First Conference 2022, LCSA of a Pedestrian Bridge made from Wind Blades, Angie Nagle (Paul Leahy presented), Video

Video of 12 mins 46 seconds, on Life Cycle Sustainability Assessment (LCSA) of a Pedestrian Bridge made from Discarded Wind Blades, written by Angie Nagle and presented by Paul Leahy, her supervisor.

opencc-by-4.0Jul 2024View details →

zenodo36/100

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms. Metabolite profiling analysis Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v). 3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode. 9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005). <div> <div> #heatmap of fold changes  </div> <div> ts <-read.csv("all_log2fc_heat_all.csv", sep=";", fileEncoding='latin1',check.names=F, header=TRUE)  </div> <div> ts1 <- ts  </div> <div> ts1$class <- NULL   </div> <div>   </div> <div> row.names(ts1) <- ts1$compound  </div> <div> ts1$compound <- NULL  </div> <div> m <- as.matrix(ts1)  </div> <div>   </div> <div> ann <- ts[1:2]  </div> <div> row.names(ann) <- ann$compound  </div> <div> ann$compound <- NULL  </div> <div>   </div> <div> install.packages("pheatmap")  </div> <div>   </div> <div> library("pheatmap")  </div> <div>   </div> <div> m1 <- m[rownames(ann), ]  </div> <div>   </div> <div> pheatmap(m1, annotation_row = ann, cluster_rows = F,cluster_cols = F, cellheight = 10,   </div> <div>          cellwidth = 10, gaps_row = cumsum(c(3,105,30,11,21,4,32,5,17)), gaps_col = cumsum(c(2,2,2)), scale = 'none')  </div> <div>   </div> <div> #boxplots  </div> <div> install.packages("ggplot2")  </div> <div> install.packages("readr")  </div> <div>   </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div>   </div> <div> install.packages("tidyverse")  </div> <div> library(tidyverse)  </div> <div> grid <- read.csv("sec_exp1_logfc_20.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> grid_g <- grid%>% gather("compound", "value", 6:25)  </div> <div>   </div> <div> d=ggplot(data = grid_g, aes(x=compound, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d+theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#ca5826","#e4ab92", "#008080", "#99cccc"))+  </div> <div>   labs(y="log2fc(metabolite content)",fill="gtype")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 5)) +   </div> <div>   theme_classic() +  </div> <div>   coord_flip() +  </div> <div>   facet_grid(condition ~ .)  </div> <div>   </div> <div> ggsave("logfcsum_exp1_15_bri1.png", width = 10, height = 10)  </div> <div>   </div> <div> #boxplots all data  </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div> library(viridis)  </div> <div> library(tidyverse)  </div> <div> library(dplyr)  </div> <div>   </div> <div> sPCA <-read.csv("GCMS_polar.csv", sep=",", fileEncoding='latin1', check.names = F, header=TRUE)  </div> <div>   </div> <div> row.names(sPCA) <- sPCA$Sample  </div> <div> sPCA_D <- sPCA[6:155]  </div> <div> sPCA_Dlog <- log(sPCA_D)  </div> <div> sPCA_Dlog10 <- log10(sPCA_D)  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, group = sPCA$Sample_group)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(group, everything())  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, sample = sPCA$Sample)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(sample, everything())  </div> <div>   </div> <div> write.csv(sPCA_Dlog10, "GCMS_polar_lg.csv")  </div> <div>   </div> <div> sPCA_Dlog_g <- sPCA_Dlog%>% gather("compound", "value", 3:152)  </div> <div>   </div> <div> d <- ggplot(sPCA_Dlog_g, aes(x=sample, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d + theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#CC6677","#882255","#44AA99", "#117733"))+  </div> <div>   labs(y="lg(normalized metabolite content)", x= "sample" , fill="group")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1)) +   </div> <div>   theme_classic() +  </div> <div>   geom_hline(yintercept=0, linetype="dashed", color = "black")  </div> <div> #PCA  </div> <div> install.packages("plotly")  </div> <div> install.packages("ggfortify")  </div> <div> library(plotly)  </div> <div> library(ggfortify)  </div> <div>   </div> <div> sPCA <-read.csv("prim.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> sPCA <- subset(sPCA, experiment!="2")  </div> <div>   </div> <div> sPCAtd <- all_d[7:240]  </div> <div> sPCAtdn <- sPCAtd[-c(24), ]  </div> <div> sPCAn <- sPCA[-c(24), ]  </div> <div>   </div> <div> #normal  </div> <div>   </div> <div> sec.pcat <- prcomp(sPCAtd, center = TRUE,scale. = TRUE)  </div> <div>   </div> <div> summary(sec.pcat)  </div> <div>   </div> <div> p <- autoplot(sec.pcat, data = all_d, colour = 'condition', shape = 'Genotype') +   </div> <div>   geom_text(aes(label = Replicate), nudge_y = 0.02) +  </div> <div>   theme_classic() +  </div> <div>   scale_color_manual(values=c("#56B4E9", "#E69F00"))  </div> <div>   </div> <div> ggplotly(p)  </div> <div> #experiment wise fold changes  </div> <div> write.csv(sec_fc_mean, "sec_foldchange_mean.csv")  </div> <div>   </div> <div> sec_fc_exp1 <- sect[1]  </div> <div>   </div> <div> sec_fc_exp1$bri1.drought <- foldchange(sect$X72.bri1.drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought1 <- foldchange(sect$X72.bri1.drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought2 <- foldchange(sect$X72.bri1.drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$bri1.control <- foldchange(sect$X72.bri1.control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control1 <- foldchange(sect$X72.bri1.control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control2 <- foldchange(sect$X72.bri1.control.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.drought <- foldchange(sect$X72.WT..drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought1 <- foldchange(sect$X72.WT..drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought2 <- foldchange(sect$X72.WT..drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.control <- foldchange(sect$X72.WT..control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control1 <- foldchange(sect$X72.WT..control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control2 <- foldchange(sect$X72.WT..control.2, sect$median.WT.c1)  </div> <div>   </div> <div>   </div> <div> write.csv(sec_fc_exp1, "sec_fc_exp1.csv")  </div> <div>   </div> <div> #all compounds for RNAsq comarision  </div> <div>   </div> <div> all_R <-read.csv("all_RNAsq.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> all_fc_exp2 <- all_R[1]  </div> <div>   </div> <div> all_fc_exp2$drought <- foldchange(all_R$bri1_drought, all_R$WT_drought)  </div> <div>   </div> <div> all_fc_exp2$control <- foldchange(all_R$bri1_control, all_R$WT_control)  </div> <div>   </div> <div> write.csv(all_fc_exp2, "all_foldchange_exp2.csv")  </div> <div>   </div> <div> #students t test  </div> <div>   </div> <div> secfc_2_t <-read.csv("sec_exp1_logfc.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> secfc_2_t <- secfc_2_t[-2]  </div> <div> secfc_2_t <- secfc_2_t[-(14:25)]  </div> <div>   </div> <div> st2<- secfc_2_t %>% gather("compound", "value", 6:171)  </div> <div> stat.test1 <- st2 %>%  </div> <div>   group_by(compound) %>%  </div> <div>   t_test(value ~ group)  </div> <div> add_significance()  </div> <div> stat.test1  </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo36/100

Data showcase papers published in the Mining Software Repositories (MSR) conference

Data regarding data showcase papers published in the Mining Software Repositories (MSR) conference. The following data files are included. citation-table.csv: SWEBOK areas of citing studies citations.bib: Bibliographic details of citing studies citing_dp_dois_citations.txt: Citations of citing studies data_papers.bib: MSR data papers dp_dois_citations.txt: Citations of data papers false_citations.bib: Citing studies that don't actuall use data papers msr-all: Bibliographic details of all MSR papers ndp_dois_citations.txt: Citations of non-data papers ndp_rand_dois_citations.txt: Citations of a randomly chose non-data paper weighted sample self-citations.csv: Data papers citations by their authors

opencc-by-4.0Jan 2019View details →

zenodo36/100

Achille Mbembe keynote @ Strategic Narratives of Technology and Africa conference, September 2, 2017

Achille Mbembe is a Research Professor in history and politics at the Wits Institute for Social and Economic Research (WISER), University of the Witwatersrand, Johannesburg, South Africa. He is the author of many books on history, politics and critical theory.  His work has been translated in various languages, including English, German, Dutch, Spanish, Portuguese and Italian. His latest book, Politiques de l'inimitié, was published in 2016. Strategic Narratives of Technology and Africa was a conference hosted by the Critical Technical Practice laboratory at the <a href="http://www.m-iti.org">Madeira Interactive Technologies Institute</a> in Funchal, Portugal. "The conference brings scholars, technologists, and cultural producers together on the island of Madeira: a European territory off the coast of Africa, a historical site of mutual entanglement between the Atlantic continents, and a point of departure for European expansion. Here we’ll strategize ways to revisit, reframe, and recode the future of technology on and for both continents. What can African theorists, technologists, and cultural producers do to generate alternatives to the influx of neocolonial narratives of tech entrepreneurship? What are key epistemologies and ways of being which are endemic in Africa that should be offered to the world through new systems and processes? How can an African information economy avoid the dynamics of the resource curse, where connectivity is extractive and exercised upon African citizens rather than by and through them? What can Western technologists do differently, and what are the spaces for collaboration? This conference aims to reinvestigate these relationships and more in order to engender dialog between African and Western audiences and participants, who should leave Madeira equipped with new strategies and new collaborative partnerships."

opencc-by-4.0Mar 2019View details →

zenodo36/100

Inputs and outputs of conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs"

This dataset contains both the inputs and the outputs from the conference article "On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina and Boris Bellalta. The article has been sent to CSCN 2019. Regarding the input, we provide both the "input_node" and "input_system" files used by the Komondor simulator. In particular, up to 50,400 different scenarios are provided, which stand for 3 maps sizes 50 different random deployments (i.e., nodes allocation), 21 OBSS/PD values, and 16 traffic loads. More details are provided in the article.  The output files collect the results gathered for all the scenarios. In addition, we include the code files used to "post-process" all the results. Contact information: francisco.wilhelmi@upf.edu

opencc-by-4.0Dec 2018View details →

zenodo36/100

6th International Conference "Fracture Mechanics of Materials and Structural Integrity" (June 3–6, 2019, Lviv, Ukraine)

12.06.2019 03–06 червня 2019 року у м. Львові відбулася 6-та Міжнародна конференція з механіки руйнування матеріалів і цілісності конструкцій (“Fracture Mechanics of Materials and Structural Integrity”, FMSI 2019). Співорганізаторами конференції виступили Європейське товариство з цілісності конструкцій (European Structural Integrity Society, ESIS), Українське товариство з механіки руйнування матеріалів, Фізико-механічний інститут імені Г.В. Карпенка НАН України та Національний університет “Львівська політехніка”. Матеріали прес-служби НАН України

opencc-by-4.0Jun 2019View details →

zenodo36/100

FIGURE 5 in Three decades of Chondrichthyan research in Brazil assessed from conferences' abstracts: patterns, gaps, and expectations

FIGURE 5 | Women versus men as last authors in Chondrichthyan Evolution. Orange, women; blue, men.

opencc-by-4.0Jun 2023View details →

zenodo36/100

Datasets belonging to the publication "Allelic variants confer Arabidopsis adaptation to small regional environmental differences".

Datasets belonging to the publication "Allelic variants confer Arabidopsis adaptation to small regional environmental differences". A short description of each file is found below. Samples in the Variant Call Format (VCF) files are named according to how they are stored in the National Center for Biotechnology Information (NCBI) BioSample database. Detailed descriptions of how each file was generated are found in the manuscript. Dartmap_Dutch_1001G_nuclear.vcf.gz 2,712,612 bi-allelic SNPs and 353,974 bi-allelic small indels called in the DartMap panel and the Dutch 1001G accessions (collectively referred to as DartMap + 1001G panel for brevity) relative to the Arabidopsis thaliana Col-0 nuclear genome reference sequence. Dartmap_Dutch_1001G_mitochondrial.vcf.gz 231 bi-allelic SNPs and 23 bi-allelic small indels called in the DartMap panel and the Dutch 1001G accessions (collectively referred to as DartMap + 1001G panel for brevity) relative to the A. thaliana Col-0 mitochondrial genome reference sequence. Dartmap_Dutch_1001G_chloroplast.vcf.gz 400 bi-allelic SNPs and 79 bi-allelic small indels called in the DartMap panel and the Dutch 1001G accessions (collectively referred to as DartMap + 1001G panel for brevity) relative to the A. thaliana Col-0 chloroplast genome reference sequence. Dartmap_CNVs.vcf.gz 29,155 copy number variants (CNVs) called in the DartMap panel relative to the A. thaliana Col-0 reference genome. Dartmap_Dutch_1001G_neighbouring_countries_nuclear.vcf.gz Filtered VCF file containing variants of the DartMap + 1001G panel and that of neighbouring countries relative to the A. thaliana Col-0 nuclear genome reference sequence. Used for population structure analysis in the manuscript. Dartmap_Dutch_1001G_neighbouring_countries_mitochondrial.vcf.gz Filtered VCF file containing variants of the DartMap + 1001G panel and that of neighbouring countries relative to the A. thaliana Col-0 mitochondrial genome reference sequence. Used for population structure analysis in the manuscript. Dartmap_Dutch_1001G_neighbouring_countries_chloroplast.vcf.gz Filtered VCF file containing variants of the DartMap + 1001G panel and that of neighbouring countries relative to the A. thaliana Col-0 chloroplast genome reference sequence. Used for population structure analysis in the manuscript. Dartmap_GWAS_GEA.vcf.gz Filtered VCF file containing all variants of the DartMap panel that were used for all genome-wide association (GWA) and genome-environment association (GEA) analyses. DartMap_phenotypical_data.xlsx All relevant phenotypical data of the DartMap panel.  Dartmap_sample_ids_NCBI_sample_names.csv Table showing which numerical ID of each DartMap sample corresponds to which NCBI BioSample ID.

opencc-by-4.0Aug 2024View details →

ScienceDex guides

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record