Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
22,445
datasets available to search
ShareScore release 0.7.1
Dataset results
22,445 results for “diversity”
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 2
<p>These figures are an integral part of Chapter 2 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 1
<p>These figures are an integral part of Chapter 1 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 3
<p>These figures are an integral part of Chapter 3 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 6
<p>These figures are an integral part of Chapter 6 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 5
<p>These figures are an integral part of Chapter 5 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 4
<p>These figures are an integral part of Chapter 4 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 3,076 Campylobacter jejuni isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 2,794-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/4/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 3,076 <em>Campylobacter jejuni </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 476 different STs are represented in this dataset, with ST21, ST50, ST48, ST45 and ST257 being the most represented ones and, together, corresponding to 29.1% of the dataset.</p> <p>File “Cj_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Cj_profiles_wgMLST.tsv” corresponds to a tab separated file with the 2,794-loci wgMLST profiles of each solate presented in the metadata file. The files “profiles/Cj_profiles_cgMLST_95.tsv”, “profiles/Cj_profiles_cgMLST_98.tsv” and “profiles/Cj_profiles_cgMLST_100.tsv” correspond to a 1,012-loci, 987-loci and 29-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>C. jejuni</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://pubmlst.org/organisms/campylobacter-jejunicoli">PubMLST</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 3,539 samples. The majority of them are associated with the INNUENDO project (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>). The remaining ones are associated with five BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB31119">PRJEB31119</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB38253">PRJEB38253</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB40238">PRJEB40238</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4165">PRJEB4165</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA350537">PRJNA350537</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 3,076 isolates passed this curation step and were included in the final dataset. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 2,794-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/4">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 2,794-loci wgMLST profiles of the 3,076 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 1,012-loci, 987-loci and 29-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,999 Escherichia coli isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 7,601-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/5/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,999 <em>Escherichia coli </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 411 different serotypes are represented in this dataset, with O157:H7 being the most represented one, corresponding to 37.1% of the dataset.</p> <p>File “Ec_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Ec_profiles_wgMLST.tsv” corresponds to a tab separated file with the 7,601-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Ec_profiles_cgMLST_95.tsv”, “profiles/Ec_profiles_cgMLST_98.tsv” and “profiles/Ec_profiles_cgMLST_100.tsv” correspond to a 2,826-loci, 2,704-loci and 465-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>E. coli </em>genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/ecoli">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 2,688 samples associated with three BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA230969">PRJNA230969</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB27020">PRJEB27020</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248042">PRJNA248042</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,999 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with <a href="https://github.com/B-UMMI/seq_typing">seq_typing</a> v2.2. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 7,601-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/5">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 7,601-loci wgMLST profiles of the 1,999 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 2,826-loci, 2,704-loci and 465-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats
<p>R Studio codes and ASV table used to analyze the microbiome data of our Echium vulgare microbial ecology experiment. </p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Landscape composition and shannon diversity of landuse classes aggregated from corine 2018 in 100 - 5000 meter radius areas around Landklif study plots
<p><span>Based on CORINE land cover data from 2018, we aggregated the original land use classes into 8 classes: urban, agriculture, grassland, Broad-leaved forest, Coniferous forest, Mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). We calculated the landscape composition (percentage of each land use type according to corine land type) for 100 - 5000 meters (100-1000 meter with 100 meter intervals, 1000 - 5000 meter with 500 meter interval) buffer area around landklif plots</span>.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
DATASET Invertebrate sounds from photic to mesophotic coral reefs reveal vertical stratification and diel diversity
<p>This dataset contains 17 wave folders. The original files were used for the study published by Raick et al. (2024) in Oecologia (10.1007/s00442-024-05572-5), while subsampled versions of these files were used for the studies published by Raick et al. (2023) in Coral Reefs (10.1007/s00338-022-02343-7) and Raick et al. (2023) in Scientia Marina (10.3989/scimar.05395.078).</p>
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</p>
Agriculture - General: biological diversity 6
<p>A database of tree of biological, cultural, ecological or historical interest because of their age, size or condition. National Biodiversity Data Centre (2016). Heritage Trees of Ireland. Occurrence dataset <a href="https://doi.org/10.15468/9athfc">https://doi.org/10.15468/9athfc</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 4
<p>The records in this dataset are general marine and coastal records of different taxonomic groups submitted to the National Biodiversity Data Centre. National Biodiversity Data Centre (2018). Coastal and Marine Species Database. Occurrence dataset <a href="https://doi.org/10.15468/oynwkx">https://doi.org/10.15468/oynwkx</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 3
<p>Data on the distribution of Irish CWR species. Data on key ITPGRA species were compiled in 2010 from the National Parks and Wildlife Service, the National Herbarium, and the National Vegetation Database. The database also includes recent CWR data collected under projects funded by DAFM (Genetic Heritage Ireland 2009-2010; and the National Biodiversity Data Centre 2011 & 2012). National Biodiversity Data Centre (2016). Irish Crop Wild Relative Database. Occurrence dataset <a href="https://doi.org/10.15468/lohime">https://doi.org/10.15468/lohime</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity
<p>The records in this dataset are general records of different taxonomic groups submitted to the National Biodiversity Data Centre. This provides a temporary facility to store and make available data submitted to the Centre, until such time as subsets of the data can be added to a recognised national database. National Biodiversity Data Centre (2016). General Biodiversity Records from Ireland. Occurrence dataset <a href="https://doi.org/10.15468/w8q1jm">https://doi.org/10.15468/w8q1jm</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 2
<p>The records in this dataset are general records of different taxonomic groups submitted to the National Biodiversity Data Centre. This provides a temporary facility to store and make available data submitted to the Centre, until such time as subsets of the data can be added to a recognised national database. National Biodiversity Data Centre (2016). General Biodiversity Records from Ireland. Occurrence dataset <a href="https://doi.org/10.15468/w8q1jm">https://doi.org/10.15468/w8q1jm</a> accessed via GBIF.org</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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.
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.
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.
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.