Find research datasets worth reusing

Linked collectors and determiners for: NEON Biorepository Aquatic Plant, Bryophyte, Lichen and Macroalgae Collection (Herbarium Vouchers [Standard Sampling]).

Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Aquatic Plant, Bryophyte, Lichen and Macroalgae Collection (Herbarium Vouchers [Standard Sampling])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252">https://bionomia.net/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252">https://gbif.org/dataset/dd1b2dbc-1a38-4212-a3d9-893a160da252</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: NEON Biorepository Terrestrial Plant Collection (Herbarium Vouchers).

Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Terrestrial Plant Collection (Herbarium Vouchers)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3fed506c-d04f-4cd4-b373-bd602572f39b">https://bionomia.net/dataset/3fed506c-d04f-4cd4-b373-bd602572f39b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3fed506c-d04f-4cd4-b373-bd602572f39b">https://gbif.org/dataset/3fed506c-d04f-4cd4-b373-bd602572f39b</a>. Formatted as a Frictionless Data package.

opencc-zeroFeb 2024View details →

Linked collectors and determiners for: NEON Biorepository Aquatic Plant, Bryophyte, Lichen, and Macroalgae Collection (Herbarium Vouchers [Clip Harvests]).

Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Aquatic Plant, Bryophyte, Lichen, and Macroalgae Collection (Herbarium Vouchers [Clip Harvests])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1c99ecf5-672b-4af9-a532-8b28af9999bc">https://bionomia.net/dataset/1c99ecf5-672b-4af9-a532-8b28af9999bc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1c99ecf5-672b-4af9-a532-8b28af9999bc">https://gbif.org/dataset/1c99ecf5-672b-4af9-a532-8b28af9999bc</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: NEON Invertebrate Voucher Collection at Arizona State University.

Natural history specimen data linked to collectors and determiners held within, "NEON Invertebrate Voucher Collection at Arizona State University". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/69f3ca43-ed03-4ed1-92c4-58f9bd0eafc1">https://bionomia.net/dataset/69f3ca43-ed03-4ed1-92c4-58f9bd0eafc1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/69f3ca43-ed03-4ed1-92c4-58f9bd0eafc1">https://gbif.org/dataset/69f3ca43-ed03-4ed1-92c4-58f9bd0eafc1</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: NEON Biorepository Mammal Collection (DNA Extracts).

Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Mammal Collection (DNA Extracts)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/66da3373-5cca-4d00-a477-af751b5ed052">https://bionomia.net/dataset/66da3373-5cca-4d00-a477-af751b5ed052</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/66da3373-5cca-4d00-a477-af751b5ed052">https://gbif.org/dataset/66da3373-5cca-4d00-a477-af751b5ed052</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: NEON Biorepository Aquatic Plant, Bryophyte, Lichen, and Macroalgae Collection (Herbarium Vouchers [Point Counts]).

Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Aquatic Plant, Bryophyte, Lichen, and Macroalgae Collection (Herbarium Vouchers [Point Counts])". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3f5df259-243c-4c32-9520-921c89fe689f">https://bionomia.net/dataset/3f5df259-243c-4c32-9520-921c89fe689f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3f5df259-243c-4c32-9520-921c89fe689f">https://gbif.org/dataset/3f5df259-243c-4c32-9520-921c89fe689f</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Jul 2023View details →

Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"

Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript.

dryad40/100

R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys

Open the record for dataset details and reuse information.

publicApr 2022View details →

openCustomFeb 2023View details →

Lab Standards for Benthic Macroinvertebrate Sequencing (repackaging of occurrences published by the NEON Biorepository Data Portal)

These DNA extracts are community standard or mock standard of macroinvertebrate DNA created at NEON headquarters, and used as a positive control for macroinvertebrate and zooplankton metabarcoding data.

openCC0Apr 2023View details →

NEON lakes logged multisonde data

This repository contains logged multisonde water quality data from National Ecological Observatory Network (NEON) lake sites. As of 1 May 2023 this data has not yet been ingested into NEON's data processing pipeline or published on the NEON data portal (data.neonscience.org). It is being provided to help fill these gaps until it can be ingested. This is raw, level 0 data which has not been QAQC'ed, and NEON makes no guarantees regarding its quality. Any questions regarding this data should be directed to the NEON staff listed in the Contacts.

openCC (other)Jul 2020View details →

Biogeochemical and 13C NMR data from NEON surface mineral soils

To understand controls on soil organic matter chemical composition across North America, we collected 13C NMR spectra and conducted and synthesized additional biogeochemical measurements from NEON Megapit soil samples as well as additional samples (total n = 42). This dataset supports the findings described in the associated manuscript by Hall, Ye et al. (2020).

openCC0Mar 2021View details →

temporalNEON: Repository containing raw and cleaned-up organismal data from the National Ecological Observatory Network (NEON) useful for evaluating the links between change in biodiversity and ecosystem stability

Organismal data include the following taxonomic groups: small mammals, fish, ground beetles, and aquatic macroinvertebrates. Data were retrieved from the National Ecological Observatory Network (NEON) database in November 2020. We submit both raw data retrieved from NEON as .rds files, R code used to process these data, as well as processed data as .csv files.

opencc-by-4.0Apr 2020View details →

TagSeq for gene expression in non-model plants: a pilot study at the Santa Rita Experimental Range NEON core site

TagSeq analysis scripts and assembled transcriptomes for four vascular plant species from the Santa Rita Experimental Range, AZ. Transcriptomes for each species were sequenced and assembled as described below. Additional details available in the associated manuscript: MS LINK. Raw reads for each available at NCBI BioProject #PRJNA599443.   Taxon selection and sampling  This study focused on four commonly-occurring species at the Santa Rita Experimental Range Long Term Research and Core NEON site (SRER). These include the native species Tidestromia lanuginosa (Nutt.) Standl. (Amaranthaceae; ‘woolly tidestromia’), Parkinsonia florida (Benth. ex A. Gray) S. Watson. (Fabaceae; ‘blue palo verde’), and Bouteloua aristidoides (Kunth) Griseb. (Poaceae; ‘needle grama’), as well as the introduced species Eragrostis lehmanniana Nees (Poaceae; ‘Lehmann lovegrass’; native to southern Africa). All species were identified using a combination of the historical flora of the Santa Rita Experimental Range (Medina, 2003), the Arizona Flora (Kearney et al., 1960), and the Flora of North America (Flora of North America Editorial Committee, eds. 1993). Vouchers were deposited in the University of Arizona herbarium (ARIZ). Tissue from mature plants was collected from an apparently healthy individual representing each target species during the 2017 growing season. An entire stem was sampled for B. aristidoides (with flowers and fruits) and E. lehmanniana (without flowers or fruits). Leaves and leaflets only were sampled for P. florida and T. lanuginosa.   RNA extraction and RNA-seq Total RNA was extracted from tissue using the Spectrum Plant Total RNA Kit (Sigma-Aldrich Co., St. Louis, MO, USA) following Protocol A. RNA was used to prepare cDNA using Nugen’s Ovation RNA-Seq System via single primer isothermal amplification (Catalogue # 7102-A01) and automated on the Apollo 324 liquid handler (Wafergen). cDNA was quantified on the Nanodrop (Thermo Fisher Scientific) and was sheared to approximately 300 bp fragments using the Covaris M220 ultrasonicator. Libraries were generated using Kapa Biosystem’s library preparation kit (KK8201). Fragments were end repaired and A-tailed, and individual indexes and adapters (Bioo, catalogue #520999) were ligated on each separate sample. The adapter ligated molecules were cleaned using AMPure beads (Agencourt Bioscience/Beckman Coulter, A63883), and amplified with Kapa’s HIFI enzyme (KK2502). Each library was then analyzed for fragment size on an Agilent’s Tapestation, and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on Thermo Fisher Scientific’s Quantstudio 5 before multiplex pooling (13-16 samples per lane) and paired-end sequencing at 2x150 bp on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using fastQC (Andrews, 2010).   De novo transcriptome assembly Raw sequence reads were processed using the SnoWhite pipeline (Barker et al., 2010a; Dlugosch et al., 2013), which included trimming adapter sequences and bases with a quality score below 20 from the 3' ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust (Lassmann et al., 2009), and removing polyA/T tails with SeqClean (https://sourceforge.net/projects/seqclean/). All transcriptomes were assembled with SOAPdenovo-Trans v1.03 (Xie et al., 2014) using a k-mer of 57. Assembled sequences for each species are in the files ending ".scafSeq".   Protein Translations We used TransPipe (Barker et al., 2010) to identify plant proteins within the assembled transcripts for each reference transcriptome and provide protein and in-frame nucleic acid sequences for each species. The reading frame and protein translation for each sequence was identified by comparison to protein sequences from 25 sequenced and annotated plant genomes from Phytozome (Goodstein et al., 2012). Using BLASTX (Wheeler et al., 2008), best hit proteins were paired with each gene at a minimum cutoff of 30% sequence similarity over at least 150 sites. Genes that did not have a best hit protein at this level were removed. To determine the reading frame and generate estimated amino acid sequences, each gene was aligned against its best hit protein by Genewise 2.2.2 (Birney et al., 2004). Based on the highest scoring Genewise DNA-protein alignments, stop and 'N' containing codons were removed to produce estimated amino acid sequences for each gene. Output included paired DNA and protein sequences with the DNA sequence reading frame corresponding to each protein sequence. Nucleic acid sequence files end in “.fna”, whereas amino acid sequence files end in “.faa”. Numbers of sequences in each of these files correspond to the position of the sequence in the associated assembly file.   Custom scripts “removePCRdups57.pl” is a Perl script that takes an input FASTQ file and removes exact duplicates identified over a supplied length at the beginning (3’ end) of the read.  Run: perl removePCRdups57.pl <inputFASTQ> <length>   “create_GTF.pl” is a Perl script that takes an input FASTA file and creates a GTF file suitable for input into HtSeq-count v.0.5.4 (Anders et al., 2015). Run: perl create_GTF.pl <inputFASTA>   “combine_HtSeq.pl” is a Perl script that takes a set of htseq output files and makes a tab delim table of counts with header of sample names and first col of row names. The input file list file should be a text file with lists of Htseq files to combine on each line, where lines are tab delimited of the following form:    <NameForOutputFile> <firstHtseqFile> <NextHtseqFile> <...etc...> Run: perl combine_HtSeq.pl <inputFileList>

opencc-by-4.0Apr 2020View details →

NEON Tree Crowns Dataset

Abstract The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location.  How can I see the data? A web server to look through predictions is available through <a href="http://idtrees.org">idtrees.org</a> Dataset Organization The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: <a href="https://data.neonscience.org/data-products/explore?search=camera">https://data.neonscience.org/data-products/explore?search=camera</a>. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see <a href="https://www.neonscience.org/field-sites/field-sites-map">https://www.neonscience.org/field-sites/field-sites-map</a>.  The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available: Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001) Area: The crown area in m2 of the rectangular bounding box. Label: All data in this release are "Tree". Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence) How were predictions made? The DeepForest algorithm is available as a python package: <a href="https://deepforest.readthedocs.io/">https://deepforest.readthedocs.io/</a>. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed. How were predictions validated? Please see Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061. Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv. Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309. Were any sites removed? Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request. # Contact We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org.

opencc-by-4.0Dec 2021View details →

NCAR-NEON system

Input data, evaluation data, and results from integrating National Ecological Observatory Network (NEON) measurements into single point simulations using the Community Terrestrial Systems Model (CTSM). The input data atmosphere data (datm) includes NEON meteorological measurements that provide boundary conditions for CTSM simulations. The evaluation data (eval) includes NEON eddy covariance flux data measured at each tower site, which includes energy, water vapor, and CO2 fluxes that at are time regularized with quality assurance and control flags applied. Finally, CTSM model results (ctsm) include monthly and daily history files of select variables simulated by the model at each NEON site. Additional miscellaneous data products (misc) with site information is also provided. These v2 data were created because we found errors in the values for latitude and longitude of NEON sites tower sites that were being used in CTSM simulations at the following four site: ONAQ, BLAN, ORNL, UNDE. These errors were corrected and sites rerun, with corrected data published here.

opencc-zeroNov 2023View details →

NEON Tree Species Predictions

# Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network Preprint: https://www.biorxiv.org/content/10.1101/2023.10.25.563626v1 ## Manuscript Abstract The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales allows an unprecedented view of forest ecosystems, forest restoration and responses to disturbance. To create detailed maps of tree species, airborne remote sensing can cover areas containing millions of trees at high spatial resolution. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual tree species using ground truthed data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees for 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1km^2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species. All predictions were uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. These data can be used to study forest macro-ecology, functional ecology, and responses to anthropogenic change. ## Data Summary Each NEON site is a single zip archive with tree predictions for all available data. For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites. For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file. ## Prediction metadata *Geometry* A four pointed bounding box location in utm coordinates. *indiv_id* A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile.  *sci_name* The full latin name of predicted species aligned with NEON's taxonomic nomenclature.  *ens_score* The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model.  *bleaf_taxa* Highest predicted category for the broadleaf submodel *bleaf_score* The confidence score for the broadleaf taxa submodel  *oak_taxa* Highest predicted category for the oak model  *dead_label* A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead. *dead_score* The confidence score of the Alive/Dead prediction.  *site_id* The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *conif_taxa* Highest predicted category for the conifer model *conif_score* The confidence score for the conifer taxa submodel *dom_taxa* Highest predicted category for the dominant taxa mode submodel *dom_score* The confidence score for the dominant taxa submodel ## Training data The crops.zip contains pre-cropped files. 369 band hyperspectral files are numpy arrays. RGB crops are .tif files. Naming format is  <individualID>_<year>_<sensor>, for example. "NEON.PLA.D07.GRSM.00583_2022_RGB.tif" is RGB crop of the predicted crown of NEON data from Great Smoky Mountain National Park (GRSM), flown in 2022. Along with the crops are .csv files for various train-test split experiments for the manuscript. ### Crop metadata There are 30,042 individuals in the annotations.csv file. We keep all data, but we recommend a filtering step of atleast 20 records per species to reduce chance of taxonomic or data cleaning errors. This leaves 132 species. *score* This was the DeepForest crown score for the crop. *taxonID* For letter species code, see NEON plant taxonomy for scientific name: https://data.neonscience.org/taxonomic-lists *individual* unique individual identifier for a given field record and crown crop *siteID* The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *plotID* NEON plot ID within the site. For more information on NEON sampling see: https://www.neonscience.org/data-samples/data-collection/observational-sampling/site-level-sampling-design *CHM_height* The LiDAR derived height for the field sampling point. *image_path* Relative pathname for the hyperspectral array, can be read by numpy.load -> format of 369 bands * Height * Weight *tile_year*  Flight year of the sensor data *RGB_image_path* Relative pathname for the RGB array, can be read by rasterio.open() # Code repository The predictions were made using the DeepTreeAttention repo: https://github.com/weecology/DeepTreeAttention Key files include model definition for a [single year model](https://github.com/weecology/DeepTreeAttention/blob/main/src/models/Hang2020.py) and [Data preprocessing](https://github.com/weecology/DeepTreeAttention/blob/cae13f1e4271b5386e2379068f8239de3033ec40/src/utils.py#L59).

opencc-by-4.0Dec 2021View details →

Calculated values of the third dielectric virial coefficient of helium-4, neon-20, and argon-40

Calculated values of the third dielectric virial coefficient of helium-4 (file Ceps_He4.dat), neon-20 (file Ceps_Ne20.dat), and argon-40 (file Ceps_Ar40.dat) ASCII format. Each file has a header whose lines begin with # describing the content. For details on the calculation see <a href="https://doi.org/10.1063/5.0077684">https://doi.org/10.1063/5.0077684</a> or <a href="https://arxiv.org/abs/2111.02691">https://arxiv.org/abs/2111.02691</a>

opencc-by-4.0Jan 2022View details →

RELEASE-2022 and provisional data for NEON DP1.20264.001 at BARC, SUGG, CRAM, LIRO, PRLA, and PRPO

NEON DP1.20264.001: Temperature at specific depth in surface water for BARC, SUGG, CRAM, LIRO, PRLA, and PRPO accessed on January 29, 2022 using the neonstore package on January 29, 2022.  The following code was use to export the data from neonstore <pre><code>neonstore::neon_export(archive = "neonstore.zip", product = "DP1.20264.001", table = "TSD_30_min-basic")</code></pre> More information about the data product can be found here:<a href="https://data.neonscience.org/data-products/DP1.20264.001"> https://data.neonscience.org/data-products/DP1.20264.001</a> The data include data from RELEASE-2022 and provisional data.  The use of provisional data necessates the generation of this Zenado object to ensure reproducibility.   Citations for data: NEON (National Ecological Observatory Network). Temperature at specific depth in surface water (DP1.20264.001). https://data.neonscience.org (accessed January 29, 2022) NEON (National Ecological Observatory Network). Temperature at specific depth in surface water, RELEASE-2022 (DP1.20264.001). https://doi.org/10.48443/g7bs-7j57. Dataset accessed from https://data.neonscience.org on January 29, 2022

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Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks

With the growing availability and accessibility of big data in ecology, we face an urgent need to train the next generation of scientists in data science practices and tools. One of the biggest barriers for implementing a data-driven curriculum in undergraduate classrooms is the lack of training and support for educators to develop their own skills and time to incorporate these principles into existing courses or develop new ones. Alongside the research goals of the National Ecological Observatory Network (NEON), providing education and training are key components for building a community of scientists and users equipped to utilize large-scale ecological and environmental data. To address this need, the NEON Data Education Fellows program formed as a collaborative Faculty Mentoring Network (FMN) between scientists from NEON and university faculty interested in using NEON data and resources in their ecology classrooms. Like other FMNs, this group has two main goals: 1) to provide tools, resources, and support for faculty interested in developing data-driven curriculum, and (2) to make teaching materials that have been implemented and tested in the classroom available as open educational resources for other educators. We hosted this program using an open education and collaboration platform from the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. Here, we share lessons learned from facilitating five FMN cohorts and emphasize the successes, pitfalls, and opportunities for developing open education resources through community-driven collaborations.

opencc-zeroMay 2022View details →