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Compressed COBS indexes (XZ) - Full 661k - Part 3

Compressed COBS indexes (XZ) - Full 661k - v0.0.2 – <a href="https://doi.org/10.5281/zenodo.7313926">part1</a>, <a href="https://doi.org/10.5281/zenodo.7313942">part2</a>, <a href="https://doi.org/10.5281/zenodo.7315499">part3</a> Part 3 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>. Citation: <blockquote> K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996">Efficient and robust search of microbial genomes via phylogenetic compression</a>, bioRxiv 2023.04.15.536996, 2023. <a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a> </blockquote>

Reconstitution of August SPEI3 drought index based on the δ18O in tree-ring cellulose for the Eastern Carpathian, for the period 1331-2012CE

Here we report the reconstruction of the summer (June to August) Standardized Precipitation-Evapotranspiration Index (SPEI3), for the period 1331-2012CE, for eastern Europe, based on annually-resolved stable oxygen isotope ratios (δ18O) from Pinus cembra L. tree-ring cellulose from the Călimani Mountains, Romania. Variations of the δ18O values capture the August SPEI3 changes both at high and low frequencies (from interannual to multidecadal scales). The palaeoclimate potential of stable isotopes in Pinus cembra L. (Swiss stone pine) tree-ring cellulose from the Călimani Mountains has been demonstrated by Nagavciuc et al. 2019 (DOI: 10.1002/joc.6349), showing that δ18O variability allows high-resolution paleoclimatic reconstructions over the eastern part of Europe, where few such reconstructions are available.

Market indexes information

Information about market indexes scraped from <a href="http://google.com/finance">google.com/finance</a> on November 22st, 2022. The markets studied are: <ul> <li>Market indexes: <ul> <li>Americas</li> <li>Europe, Middle East and Africa</li> <li>Asia Pacific</li> </ul> </li> <li>Most active</li> <li>Gainers</li> <li>Losers</li> <li>Climate leaders</li> <li>Crypto</li> <li>Currencies</li> </ul>

Indonesia, monthly Standardized Precipitation-Evapotranspiration Index (SPEI) blend 1960 - 2021

IDN_CLI_SPEI_blend_0p042_1961_2021 is currently the only comprehensive high resolution Indonesia gridded historical dataset of SPEI blend and available for public. The SPEI - https://spei.csic.es/ is an extension of the widely used SPI. The SPEI is designed to take into account both precipitation and potential evapotranspiration (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand. The IDN_CLI_SPEI_blend_0p042_1960_2021 is derived using precipitation and potential evapotranspiration from TerraClimate data - https://www.climatologylab.org/terraclimate.html, it has 0.042 degree gridded resolution, a monthly and available from 1958 to 2021. The calibration period is January 1961 to December 2020. The starting date of the dataset is 1960 in order to provide common information across the different SPEI time-scales. The SPEI blend integrate several SPEI scales into a single product, combine 3-, 6-, 9-, 12- and 24-month SPEI to estimate the overall dry/wet condition.  The SPEI processed using climate_indices, an open source Python library providing reference implementations of commonly used climate indices. https://pypi.org/project/climate-indices/

opencc-by-4.0Dec 2022View details →

Genome indexes for Mus musculus (mm39)

BUILDING HISAT2 INDEXES IN CSC Here is the case for house mouse genome (mm39). The genome indexing step requires big memory and it might not be possible to carry out it on a laptop. Genome indexes for Mus musculus (mm39) were created using HISAT2 v2.2.1 on CSC (IT Center for Science), thanks to CSC-Puhti.  1. Create conda environment folder file to install the required packages, install and add the bin directory to the path. mkdir STRTN-env conda-containerize new --prefix STRTN-env STRTN-env.yml export PATH="<install_dir>/STRTN-env/bin:$PATH" 2. Load the required module. module load tykky export PATH="<install_dir>/STRTN-env/bin:$PATH" module load r-env if test -f ~/.Renviron; then     sed -i '/TMPDIR/d' ~/.Renviron fi echo "TMPDIR=${WorkingDir_PATH}" >> ~/.Renviron 3. Obtain the genome sequences of reference and ERCC spike-ins. You may add the ribosomal DNA repetitive unit for human (U13369) and mouse (BK000964). wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/bigZips/mm39.fa.gz unpigz -c mm39.fa.gz | ruby -ne '$ok = $_ !~ /^>chrUn_/ if $_ =~ /^>/; puts $_ if $ok' > mouse_reference.fasta wget https://tsapps.nist.gov/srmext/certificates/documents/SRM2374_putative_T7_products_NoPolyA_v2.FASTA cat SRM2374_putative_T7_products_NoPolyA_v2.FASTA >> mouse_reference.fasta 4. Extract splice sites and exons from a GTF file. Here we used wgEncodeGencodeBasicVM30 as the annotation file. You may additionally perform `hisat2_extract_snps_haplotypes_UCSC.py` to extract SNPs and haplotypes from a dbSNP file for human and mouse. wget https://hgdownload.soe.ucsc.edu/goldenPath/mm39/database/wgEncodeGencodeBasicVM30.txt.gz unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_splice_sites.py - | grep -v ^chrUn > splice_sites.txt unpigz -c wgEncodeGencodeBasicVM30.txt.gz | hisat2_extract_exons.py - | grep -v ^chrUn > exons.txt 5. Build the HISAT2 index. This outputs a set of files with suffixes. Here, `mouse_reference.1.ht2`, `mouse_reference.2.ht2`, ..., `mouse_reference.8.ht2` are generated. In this case, `mouse_reference` is the basename used for `-i, --index`. hisat2-build mouse_reference.fasta --ss splice_sites.txt --exon exons.txt mouse_index/mouse_reference 6. Create the sequence dictionary for the reference and Spike-in sequences. This is required for the Picard MergeBamAlignment program. Note that the original FASTA file (`mouse_reference.fasta` here) is also required. picard CreateSequenceDictionary R=mouse_reference.fasta O=mouse_reference.dict 7. Put the genome indexes, genome fasta file, sequence dictionary to same folder. mv mouse_reference.dict mouse_reference mv mouse_reference.fasta mouse_reference

opencc-by-4.0Jan 2022View details →

Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest

Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from September 2019-December 2020 at the Southern Old Black Spruce site in Saskatchewan Canada. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and solar-induced fluorescence (SIF) and  for a stand-representative mix of black spruce and larch. Additionally, we provide photosynthetically active radiation (PAR), absorbed photosynthetically active radiation (APAR), and the escape fraction of SIF photons (fesc). Version 2 also provides half-hourly averaged SIF and SIFrelative for black spruce (evergreen) and larch (deciduous) separately.

opencc-by-4.0Feb 2023View details →

Tea bag index (TBI) for a pot trial with five plant species in Trani (Apulia, Italy)

The file contains mean k and S value per GPS location, with as meta-data the starting date, duration and biome of the study.

opencc-by-4.0Mar 2023View details →

Species-colored Themisto v3 index with 640k bacterial genomes

This is a Themisto v3 [1] index containing the 639,981 high-quality genomes from 661k bacterial genomes dataset of Blackwell et al. [2]. The index contains all distinct 31-mers of the dataset (both strands). There are 71 billion distinct 31-mers in the data (35.5 billion reverse complement pairs). Each k-mer is annotated with the set of species identifiers that contain that 31-mer. The species identifiers are called colors. There are 2340 distinct colors in the dataset, so the color identifiers range from 0 to 2339. To pseudoalign reads.fastq against the index using 16 threads, install <a href="https://github.com/algbio/themisto">Themisto v3</a>, and use the following command: <pre><code class="language-bash">themisto pseudoalign -q reads.fastq -i themisto_640k/index -t 16 --temp-dir .</code></pre> This will output one line of space-separated integers per read in the input. The first integer on a line is the zero-based rank of the read in the fastq file, and the rest of the integers are the identifiers of colors that are compatible with the read. The file color_names.csv lists the species name and the taxid for each color. The pseudoalignment counts should not be directly used as abundance estimates because they only describe which reads are compatible with which species, and a single read may be compatible with many. We recommend using mSWEEP to estimate abundances based on the pseudoalignment data: https://github.com/PROBIC/mSWEEP. -- The index was constructed with Themisto v3.0.0 using the following command line parameters: <pre><code class="language-bash">themisto build -i input_file_list.txt --file-colors --reverse-complements -o 640k_bacteria -m 512000 -t 48 -k 31 --temp-dir temp --verbose -d 20</code></pre> The file source_accessions.txt lists the accession numbers of assemblies included in the database. [1] Alanko, J. N., Vuohtoniemi, J., Maklin, T., & Puglisi, S. J. (2023). Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes. bioRxiv, 2023-02. [2] Blackwell, G. A., Hunt, M., Malone, K. M., Lima, L., Horesh, G., Alako, B. T., ... & Iqbal, Z. (2021). Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLoS biology, 19(11), e3001421.

opencc-by-4.0May 2023View details →

BF skip indexes for Ethereum

General information This repository includes all data needed to reproduce the experiments presented in [1]. The paper describes the BF skip index, a data structure based on Bloom filters [2] that can be used for answering inter-block queries on blockchains efficiently. The article also includes a historical analysis of <code>logsBloom</code> filters included in the Ethereum block headers, as well as an experimental analysis of the proposed data structure. The latter was conducted using the data set of events generated by the <a href="https://etherscan.io/address/0x06012c8cf97bead5deae237070f9587f8e7a266d">CryptoKitties Core</a> contract, a popular decentralized application launched in 2017 (and also one of the first applications based on NFTs). In this description, we use the following abbreviations (also adopted throughout the paper) to denote two different sets of Ethereum blocks. <ol> <li>D1: set of all Ethereum blocks between height 0 and 14999999.</li> <li>D2: set of all Ethereum blocks between height 14000000 and 14999999.</li> </ol> Moreover, in accordance with the terminology adopted in the paper, we define the set of keys of a block as the set of all contract addresses and log topics of the transactions in the block. As defined in [3], log topics comprise event signature digests and the indexed parameters associated with the event occurrence. Data set description <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td>filters_ones_0-14999999.csv.xz</td> <td>Compressed CSV file containing the number of ones for each logsBloom filter in D1.</td> </tr> <tr> <td>receipt_stats_0-14999999.csv.xz</td> <td>Compressed CSV file containing statistics about all transaction receipts in D1.</td> </tr> <tr> <td>Approval.csv</td> <td>CSV file containing the Approval event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Birth.csv</td> <td>CSV file containing the Birth event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Pregnant.csv</td> <td>CSV file containing the Pregnant event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>Transfer.csv</td> <td>CSV file containing the Transfer event occurrences for the CryptoKitties Core contract in D2.</td> </tr> <tr> <td>events.xz</td> <td>Compressed binary file containing information about all contract events in D2.</td> </tr> <tr> <td>keys.xz</td> <td>Compressed binary file containing information about all keys in D2.</td> </tr> </tbody> </table> File structure We now describe the structure of the files included in this repository. <ul> <li>filters_ones_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 3 columns. Note that it is not necessary to decompress this file, as the provided code is capable of processing it directly in its compressed form. The columns have the following meaning. <ol> <li><code>blockId</code>: the identifier of the block.</li> <li><code>timestamp</code>: timestamp of the block.</li> <li><code>numOnes</code>: number of bits set to 1 in the <code>logsBloom</code> filter of the block.</li> </ol> </li> <li>receipt_stats_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 5 columns. As for the previous file, it is not necessary to decompress this file. <ol> <li><code>blockId</code>: the identifier of the block.</li> <li><code>txCount</code>: number of transactions included in the block.</li> <li><code>numLogs</code>: number of event logs included in the block.</li> <li><code>numKeys</code>: number of keys included in the block.</li> <li><code>numUniqueKeys</code>: number of distinct keys in the block (useful as the same key may appear multiple times).</li> </ol> </li> <li>All CSV files related to the CryptoKitties Core events (i.e., Approval.csv, Birth.csv, Pregnant.csv, Transfer.csv) have the same structure. They consist of 1 million rows (one for each block in D2) and 2 columns, namely: <ol> <li><code>blockId</code>: identifier of the block.</li> <li><code>numOcc</code>: number of event occurrences in the block.</li> </ol> </li> <li>events.xz is a compressed binary file describing all unique event occurrences in the blocks of D2. The file contains 1 million data chunks (i.e., one for each Ethereum block). Each chunk includes the following information. Do note that this file only records unique event occurrences in each block, meaning that if an event from a contract is triggered more than once within the same block, there will be only one sequence within the corresponding chunk. <ol> <li><code>blockId</code>: identifier of the block (4 bytes).</li> <li><code>numEvents</code>: number of event occurrences in the block (4 bytes).</li> <li>A list of <code>numEvent</code> sequences, each made up of 52 bytes. A sequence represents an event occurrence and is indeed the concatenation of two fields, namely: <ol> <li>Address of the contract triggering the event (20 bytes).</li> <li>Event signature digest (32 bytes).</li> </ol> </li> </ol> </li> <li>keys.xz is a compressed binary file describing all unique keys in the blocks of D2. As for the previous file, duplicate keys only appear once. The file contains 1 million data chunks, each representing an Ethereum block and including the following information. <ol> <li><code>blockId</code>: identifier of the block (4 bytes)</li> <li><code>numAddr</code>: number of unique contract addresses (4 bytes).</li> <li><code>numTopics</code>: number of unique topics (4 bytes).</li> <li>A sequence of <code>numAddr</code> addresses, each represented using 20 bytes.</li> <li>A sequence of <code>numTopics</code> topics, each represented using 32 bytes.</li> </ol> </li> </ul> Notes For space reasons, some of the files in this repository have been compressed using the XZ compression utility. Unless otherwise specified, these files need to be decompressed before they can be read. Please make sure you have an application installed on your system that is capable of decompressing such files. Cite this work If the data included in this repository have been useful, please cite the following article in your work. <pre><code>@article{loporchio2025skip, title={Skip index: Supporting efficient inter-block queries and query authentication on the blockchain}, author={Loporchio, Matteo and Bernasconi, Anna and Di Francesco Maesa, Damiano and Ricci, Laura}, journal={Future Generation Computer Systems}, volume={164}, pages={107556}, year={2025}, publisher={Elsevier} }</code></pre> References <ol> <li>Loporchio, Matteo et al. "Skip index: supporting efficient inter-block queries and query authentication on the blockchain". Future Generation Computer Systems 164 (2025): 107556. <a href="https://doi.org/10.1016/j.future.2024.107556" target="_blank" rel="noopener">https://doi.org/10.1016/j.future.2024.107556</a></li> <li>Bloom, Burton H. "Space/time trade-offs in hash coding with allowable errors." Communications of the ACM 13.7 (1970): 422-426.</li> <li>Wood, Gavin. "Ethereum: A secure decentralised generalised transaction ledger." Ethereum project yellow paper 151.2014 (2014): 1-32.</li> </ol>

opencc-by-4.0Mar 2023View details →

Dataset for "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method"

Dataset for the paper "Future projections for the Antarctic ice sheet until the year 2300 with a climate-index method" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2023.41">doi: 10.1017/jog.2023.41</a>). Please see the README for details. V1.1: Run-specs header files for SICOPOLIS added. README updated. V1: Initial upload. * * * * * * * Users should cite the original publication when using all or parts of these data.

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)

Brief Introduction: The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.   The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.   The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.   Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details. We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)   Major updates: Version 1.0 (December 15, 2022): · The original version of the product.   Version 1.1 (June 17, 2023): · A pixel-wise Random Forests consolidation method is used to replace the linear one. · The data files have been re-organized on a decade basis.   Version 1.2 (August 17, 2023): · The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.   Dataset Characteristics: Spatial Coverage:       180ºW~180ºE, 63ºS~90ºN Projection:                  Geographic Spatial Resolution:     1/12 degree Temporal Resolution: Half month Temporal Coverage:   January 1982 to December 2022 Image Dimension:      Rows-2160; Columns-4320 Units:                          unitless Fill Value:                    65535 Data Type:                  uint16 Valid Range:               0-1000 Scale Factor:               0.001 File Format:                TIFF(.tif) File Size:                     ~8Mb each file   References: Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023. Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016. Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.

opencc-zeroAug 2023View details →

Index Österreichischer Schulen

Dies ist eine gesäuberte Version der Daten der Website schulen-online.at (vom Bundesministerium für Bildung, Wissenschaft und Forschung gehostet) Version 1.0.0 wurde am 2019-09-03 erstellt.

Supplementary Data for "Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results from a Community-Partnered Low-cost Sensor Deployment"

These are the underlying data sets needed to build the kriging maps and calculate dissemination block cumulative hazard indices described in the paper. There are three data sets: <ol> <li>"Sampling location names and coordinates.csv": locations and IDs of the low-cost sensors and the regulatory monitoring stations used in this work. [NOTE: latitudes and longitudes for the sensor deployments have been intentionally rounded to protect the location of volunteer sensor hosts.]</li> <li>"Dissemination Block Populations.csv": These are the relevant dissemination blocks in the study domain and their associated populations. This information was originally extracted from: https://censusmapper.ca/#13/49.2430/-123.1252</li> <li>"Daily average concentrations by site and pollutant.csv": This contains the PM2.5, NO2 and O3 daily averages for the entire study period across all low-cost sensor sites and regulatory monitoring stations. Refer to "Sampling location names and coordinates.csv" to parse the labels in this data set.</li> </ol> There is also a sample code in Python to construct the kriging maps provided in 2 formats. [NOTE: we have intentionally excluded uploading the exact data sets imported by this code; our original data contains exact locations of sensor host volunteers and thus cannot be shared.] <ol> <li>"Jain et al - GeoHealth - Kriging Script.ipynb": A Jupyter notebook script to import the data, build kriging maps, calculate CHIs, and export the data.</li> <li>" Jain et al - GeoHealth - Kriging Script.pdf": A PDF export of the Jupyter notebook so that you can read the Python scripts even if you are not a Jupyter notebooks user.</li> </ol>

Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2)

Brief Introduction:   The fourth generation GIMMS Leaf Area Index product (GIMMS LAI4g, version 1.2) provides spatiotemporally consistent global LAI data in half-month and 1/12° from 1982 to 2020. It is created to address two major uncertainties presented in current global long-term LAI products, i.e., (1) the effects of NOAA satellite orbital drift and AVHRR sensor degradation and (2) insufficient LAI reference data to build robust LAI model particularly before the late 1990s.   The GIMMS LAI4g was generated based on biome-specific BPNN models that employed the latest PKU GIMMS NDVI product and 3.6 million high-quality global Landsat LAI samples. It was then consolidated with the Reprocess MODIS LAI to extend the temporal coverage to 2020 via a pixel-wise Random Forests fusion method.   The GIMMS LAI4g exhibits overall high accuracy and low underestimation evaluated by field LAI measurements and Landsat LAI samples. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency before and after the year 2000 and a more reasonable global vegetation trend. It could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and benefit the model development in Earth and environmental sciences.   Here we provide two versions of GIMMS LAI4g for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the Reprocess MODIS LAI (1982−2020). We strongly recommend an adequate use of the quality control (QC) layer in the product. Please refer to the Readme file for more details.   Major updates: Version 1.0 (February 17, 2023): · The original version of the product.   Version 1.1 (June 14, 2023): · The GIMMS LAI4g is now validated by ground LAI measurements. · A pixel-wise Random Forests consolidation method is used to replace the linear one. · Two versions of GIMMS LAI4g are now available, one solely based on AVHRR data and one consolidated with MODIS LAI.   Version 1.2 (August 25, 2023): · The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate LAI values during 1982−1984 for all biomes, October−April for EBF, and winters for ENF, when the Landsat NDVI samples were absent or relatively scarce.   Dataset Characteristics: Spatial Coverage:       180ºW~180ºE, 63ºS~90ºN Projection:                  Geographic Spatial Resolution:     1/12 degree Temporal Resolution: Half month Temporal Coverage:   January 1982 to December 2020 Image Dimension:      Rows-2160; Columns-4320 Units:                          m2/m2 Fill Value:                   65535 Data Type:                  uint16 Valid Range:              0-7000 Scale Factor:                     0.001 File Format:                TIFF(.tif) File Size:                    ~8Mb each file   References: Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-68, in review, 2023.

Data base of the complexity Indexes to compute MFA and MCI from the paper "Unleashing The Potential Of Artificial Reefs Design"

This data base compiles the Complexity indexes used to compute the MFA and extract the MCI from the paper "Unleashing The Potential Of Artificial Reefs Design: A Purpose-Driven Evaluation Of Structural Complexity" (https://doi.org/10.32942/X2G300)

openCC0Oct 2021View details →

A search index for the ecocomDP R package

This data package contains summary information of datasets in the ecocomDP format published by EDI and NEON. This summary information is formatted in a search index used by the search function of the ecocomDP R package.

openOpenDec 2015View details →

Harvest data including the shoot leaf area index, position in the canopy, and shoot and plant tissue area, count and mass for each shoot harvested at three levels in the canopy from 19 1m x 1m plots near LTER Shrub plots, Toolik Field Station, AK 2012.

Leaf and plant tissue area and mass from shoots harvested from 19 1m x 1m point frame plots near Toolik Field Station, AK during the summer of 2012. Six shoots were harvested from each plot, two from each canopy layer: upper, middle, and low. Each shoot came from a different plant, and the species selected was based on the species dominant in that canopy layer. The leaf area and mass were used to correct A/Ci and light response curves taken on each shoot [data published separately]. At the time of collection, the location relative to the point frame, height, and leaf area index (LAI) of each shoot was measured; those data are included here.

openOpenDec 2015View details →

Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Field Station, Imnavait, and Sag river DOT sites, in the northern foothills of the Brooks Range, Alaska, summer 2010-2014.

Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Lake Field Station, Imnavait Creek and Sagavanirktok River DOT sites in the northern foothills of the Brooks Range, Alaska. Located south of the Arctic LTER and Toolik Lake Field Station. Data collected from May to July 2010-2014. Methods and further data published in Ecography by Rich, et al. 2013.

openCC (other)Jan 2020View details →

Normalized difference vegetation index and Leaf area index of tussocks from reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon, Alaska 2016

Normalized difference vegetation index (NDVI) and Leaf area index (LAI) data from tussocks in the reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon in 2016.