AndreaBozzo/ceres-open-data-index huggingface dataset:
PhenoCam images from JERWERN site, Jornada Experimental Range, New Mexico, USA since 2017

<p>This data set consists of repeat digital imagery from the tower-mounted digital cameras (hereafter, PhenoCams) at the Jornada Experimental Range. JER is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes. The archived images provide a permanent record that can be visually-inspected to determine the phenological state of the vegetation at any point in time. Vegetation greenness metrics (e.g., GCC) derived from the ratio of the green color band to sum of red, green, and blue color bands serve as proxies for vegetation greenness.  Greenness metrics can be extracted from the images using simple image processing methods in 1-day or 3-day increments.</p>
<p>This dataset is available to the public and may be freely downloaded. Please keep the designated Contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.</p>
<p>The development of PhenoCam has been supported by multiple entities. Those include the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (award EF-1065029), DOE’s Regional and Global Climate Modeling program (award DE-SC0016011), the US National Park Service Inventory and Monitoring Program, the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey), and is currently supported by the Department of Agriculture (USDA) Agricultural Research Service.</p>
<p>This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR and the Jornada Experimental Range are supported by the United States Department of Agriculture (USDA) Agricultural Research Service.</p>

Tags: ars, data-gov, digital-image, long-term-monitoring-sites, ltar, new-mexico, np216, phenocam, phenology, primary-productivity
Organization: Department of Agriculture
Protein predictions for Calonectria pseudonaviculata CBS 139707 (aka cpsCT01)

<p>Boxwood blight disease, caused by the fungi Calonectria henricotiae and C. pseudonaviculata, is an emergent threat to natural and managed landscapes worldwide. This dataset contains protein predictions and identifications generated from Calonectria pseudonaviculata CBS 139707 (aka cpsCT01) genome dataset <a href="https://doi.org/10.15482/USDA.ADC/1410184">https://doi.org/10.15482/USDA.ADC/1410184</a>.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Protein predictions and identifications generated from Calonectria pseudonaviculata CBS 139707 (aka cpsCT01) genome dataset .</p> <p>File Name: Calonectria pseudonaviculata protein.fasta.txt</p><p>Resource Description: This dataset contains protein predictions and identifications generated from Calonectria pseudonaviculata CBS 139707 (aka cpsCT01) genome dataset <a href="https://doi.org/10.15482/USDA.ADC/1410184">https://doi.org/10.15482/USDA.ADC/1410184</a></p></li></ul><p></p>

Tags: ars, boxwood-blight, calonectria-pseudonaviculata, data-gov, fungus, np303, plant-pathogens
Organization: Department of Agriculture
Data from: Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination

<p>Pollen data from the manuscript titled: Identity and diversity of pollens collected by two managed bee species while in blueberry fields for pollination, published in Environmental Entomology. Pollen was collected from honey bee (Apis mellifera) and bumble bee (Bombus impatiens) forager returning to the colony, while in commercial highbush blueberry fields in SW Michigan for crop pollination. Pollen was identified visually using morphological features.</p>
<p>Funding also provided by Project GREEEN award GR18-038. Project GREEEN, Michigan’s plant agriculture initiative housed at Michigan State University, is a cooperative effort by plant-based commodity groups and businesses in cooperation with Michigan State University AgBioResearch, Michigan State University Extension and the Michigan Department of Agriculture and Rural Development to advance Michigan’s economy through plant-based agriculture. </p>

Tags: ars, blueberry, bumble-bees, data-gov, honey-bees, np305, pollen, pollination
Organization: Department of Agriculture
Data from: Thermal tolerance of western corn rootworm: critical thermal limits, knock-down resistance, and chill coma recovery

<p>Data files for manuscript titled "Thermal tolerance of western corn rootworm: critical thermal limits, knock-down resistance, and chill coma recovery".</p>
<p>Metadata is contained within excel file that describes all variables for each tab.</p>
<p>Abstract from paper:
Western corn rootworm, <em>Diabrotica virgifera virgifera</em>, is one of the most economically important crop pests in the world with estimates of damage and control approximating over $1 billion USD annually. Despite an abundance of research devoted to studying rootworm biology in the central Corn Belt of the United States, key aspects on their thermal ecology are still lacking. Here we address this knowledge gap by measuring critical thermal limits, knock-down resistance, and chill coma recovery. In doing so, we also address methodological questions surrounding measurements of thermal tolerance using a variety of dynamic and static assays. The average critical thermal maxima across all trials was 43.0°C, while the average critical thermal minima was 2.5°C. Critical thermal limits were relatively invariant across all treatments except at faster ramping rates. Knock-down resistance decreased with increasing temperature as survival dropped from 100% at 39°C to 0% within 10 minutes at 44°C. Recovery from chill coma increased by 1.62 minutes for each hour of exposure at 0°C, while survival decreased by 50% after only 24 hours. Combined, our results present the first composite picture of different thermal traits for western corn rootworm, which will be vital for predicting their survival and potential spread under future climate change scenarios. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Data for "Thermal tolerance of western corn rootworm: critical thermal limits, knock-down resistance, and chill coma recovery".</p> <p>File Name: WCR Thermal Methods - Final Data File.xlsx</p><p>Resource Description: Excel file with 6 tabs: Metadata, Start Temperature, Ramping Rate, Knock-down Resistance, Chill Coma Survival, and Chill Coma Recovery. </p></li></ul><p></p>

Tags: ars, ctmax, ctmin, data-gov, diabrotica-virgifera-virgifera, np304, ramping-rate, temperature
Organization: Department of Agriculture
Human blood plasma oxylipins + endocannabinoids exercise patterns

<p>These data represent temporal changes in the concentrations of individual oxylipins (OxL) and endocannabinoids (eCB) in adult women during a sub-maximal aerobic exercise bout, and at recovery (20 min. post-cessation of exercise) (Supplementary Materials 1).  Results are depicted both before (pre-) and after (post-) a weight loss and fitness intervention lasting at least 14 wk.  Participants were obese, sedentary, and insulin resistant in the pre-intervention phase.  Also depicted are statistical groupings of metabolites, as an Excel file (Supplementary Materials 2). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Supporting Materials 1.</p> <p>File Name: oxy_timecourse, SUPPLEMENTAL MATERIALS 1, for submission.pdf</p><p>Resource Description: Blood plasma oxylipin and endocannabinoid concentrations over time during an acute sub-maximal aerobic exercise bout in women, both before and after a 14 wk+ fitness and weight loss intervention.  Total exercise time = 30 min, followed by a recovery period of 20 min.</p><p>Resource Software Recommended: Adobe Acrobat,url: <a href="https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html">https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html</a> </p></li><br><li><p>Resource Title: Supporting Materials 2, oxylipin and endocannabinoid statistics & clustering.</p> <p>File Name: SUPPLEMENTAL MATERIALS 2, for submissionXLS.xls</p><p>Resource Description: Excel sheet with statistics and statistical pattern clustering associated with blood plasma oxylipins and endocannabinoids in women performing 30 min. sub-maximal aerobic exercise, followed by a 20 min recovery period.</p><p>Resource Software Recommended: Microsoft Excel,url: <a href="https://products.office.com/en-us/excel">https://products.office.com/en-us/excel</a> </p></li></ul><p></p>

Tags: ars, data-gov, lipoxygenase, np107, oxylipid, polyunsaturated-fatty-acid, pufa, soluble-epoxide-hydrolase
Organization: Department of Agriculture
Data from: A First Look at the Ability to Use Genomic Prediction for Improving Ratooning Ability of Sugarcane

<p>The filtered VCF file of 10501 SNPs from the 432 individuals in this study.  This dataset was used for genome-wide association study (GWAS) and genomic prediction of sugarcane ratooning ability. It was developed using Rapid Genomics Capture-Seq technology. </p>
<p>Resources in this dataset:</p>
<ul>
<li>Resource Title: SNP data for 432 Sugarcane Clones
File Name: sugarcane.10501.SNPs<em>.432.Inds</em>.vcf</li>
</ul>

Tags: ars, candidate-gene, data-gov, genomic-selection, genomics, gwas, molecular-trait-association, np301, ratooning, sugarcane, sugarcane-production
Organization: Department of Agriculture
Data from: Characterization of commercial cricket protein powder and impact of cricket protein powder replacement on wheat dough protein composition

<p>Data from publication characterizing two commercially available cricket protein powders and their impact on the molecular weight distribution when incorporated into wheat dough.</p>
<p>Data collected from duplicate laboratory analysis of protein composition of cricket protein powders, wheat flour, and wheat dough mixed with cricket protein powders. Samples were analyzed using gel electrophoresis and size exclusion chromatography along with differential solubility. Samples were analyzed to determine the effect of cricket protein powder on the solubility and molecular weight of wheat flour proteins when mixed into a dough. </p>
<p>Resources in this dataset:</p>
<ul>
<li>
<p>Resource Title: Readme.txt file
File Name: ReadMe.txt
Resource Description: Description of data included.</p>
</li>
<li>
<p>Resource Title: Figure 1. Size exclusion chromatographs of cricket protein powders
File Name: Figure1.JPG
Resource Description: Graph of size exclusion chromatographs of cricket protein powders</p>
</li>
<li>
<p>Resource Title: Figure 2: Size exclusion chromatographs of wheat flour and cricket protein powder fractions.
File Name: Figure2.JPG
Resource Description: Size exclusion chromatographs of soluble and insoluble protein fractions from cricket powder and wheat flour. Both with disulfide bonds reduced ("reduced") and intact ("unreduced").</p>
</li>
<li>
<p>Resource Title: SDS page analysis of wheat flour and cricket protein powders
File Name: Figure3.JPG
Resource Description: Photograph of an SDS-PAGE gel showing separation of proteins extracted from wheat flour and two commercially available cricket protein powders</p>
</li>
<li>
<p>Resource Title: Data definitions for SEC peak area data file
File Name: data definitions.txt
Resource Description: File contains abbreviation list for SEC peak area data file</p>
</li>
<li>
<p>Resource Title: SEC peak area data
File Name: SEC peak area data.csv
Resource Description: Data file of SEC peak areas</p>
</li>
</ul>

Tags: ars, bread, cricket-protein-powder, data-gov, gluten, np306, protein
Organization: Department of Agriculture
Data and code from: AI-based image profiling and detection for the beetle byte quintet using Vision Transformer (ViT) in advanced stored product infestation monitoring

<p dir="ltr">Managing beetles that infest stored products is crucial for reducing losses in harvest supply chains and improving food security and safety. Successful pest management programs require effective and timely monitoring programs, but traditional methods for detecting pests are time- and labor-intensive and require taxonomic expertise. New, automated methods using computer vision have the potential to improve accuracy and speed of detection, but often struggle to differentiate between beetle species, which tend to be small and morphologically similar. Our research centers on five economically significant beetle species, referred to as the 'Beetle Byte Quintet,' and proposes a novel methodology leveraging Vision Transformers (ViT) to enhance the precision and robustness of their classification. The method involves using an image profiling technique to capture morphological characteristics like body shape, color and exoskeleton structures that are key for distinguishing between species. By utilizing this species profiling, the ViT model achieved an accuracy rate of over 99.34% during training and 96.57% during testing. These findings highlight the model’s ability to generalize and maintain precision with new unseen data surpassing traditional computer vision algorithms significantly. The integration of ViT can help enable real time monitoring and is adaptable to a range of pest monitoring solutions for large scale storage settings which addresses the complexities of environments. This AI driven approach not only simplifies species identification but also promotes accurate and targeted pest control practices leading to reduced economic losses and improved food security.</p><p dir="ltr">A subsample of images used in the model are included here for <i>Rhyzopertha dominica</i> (lesser grain borer), <i>Sitophilus zeamais</i> (maize weevil), <i>Tribolium castaneum</i> (red flour beetle), <i>Cryptolestes ferrugineus</i> (rusty grain beetle), and <i>Oryzaephilus surinamensis</i> (sawtoothed grain beetle). Custom MatLab code and a data descriptor README are also included.</p>

Tags: beetles, coleoptera, computer-vision, image-identification, image-processing, neural-network, pest-management, postharvest, source-code, stored-grain, stored-product-insects
Organization: Department of Agriculture
Data from: Attracted by higher crude protein, grasshopper abundance and offtake increase after prescribed fire

<p>This study examined how indirect fire effects (improved forage quality) affect the density of and offtake by grasshoppers at two different times since fire and in unburned plots. Data include total aboveground forage removal inside and outside grasshopper exclosures, crude protein content of aboveground plant material, and grasshopper densities throughout the study period. Both forage offtake and grasshopper density were significantly higher in burned plots compared to unburned plots. Burned plot grasshopper density increased over time, with greater rates of increase in recently burned plots, while density remained constant in unburned locations. These density and offtake patterns appear to be the result of higher crude protein content in burned plots, on account of them having a much higher proportion of recent growth after fire removed aboveground senesced material.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Rangeland fire grasshopper forage data.</p> <p>File Name: RangelandFireGrasshopperForage.xlsx</p><p>Resource Description: Excel file containing plot-level data used in formal analysis.</p></li><br><li><p>Resource Title: Script.</p> <p>File Name: ScriptSupplement.pdf</p><p>Resource Description: R script used for data wrangling and statistical analysis</p><p>Resource Software Recommended: R 4.0.3,url: <a href="https://cran.r-project.org">https://cran.r-project.org</a> </p></li></ul><p></p>

Tags: ars, data-gov, fire-ecology, fire-grazing-interaction, grasshopper-abundance, np215, np304, rangeland-forage-quality
Organization: Department of Agriculture
PhenoCam images from JERNOVEL2 site, Jornada Experimental Range, New Mexico, USA since 2022

<p>This data set consists of repeat digital imagery from the tower-mounted digital cameras (hereafter, PhenoCams) at the Jornada Experimental Range. JER is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes. The archived images provide a permanent record that can be visually-inspected to determine the phenological state of the vegetation at any point in time. Vegetation greenness metrics (e.g., GCC) derived from the ratio of the green color band to sum of red, green, and blue color bands serve as proxies for vegetation greenness.  Greenness metrics can be extracted from the images using simple image processing methods in 1-day or 3-day increments.</p>
<p>This dataset is available to the public and may be freely downloaded. Please keep the designated Contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.</p>
<p>The development of PhenoCam has been supported by multiple entities. Those include the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (award EF-1065029), DOE’s Regional and Global Climate Modeling program (award DE-SC0016011), the US National Park Service Inventory and Monitoring Program, the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey), and is currently supported by the Department of Agriculture (USDA) Agricultural Research Service.</p>
<p>This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR and the Jornada Experimental Range are supported by the United States Department of Agriculture (USDA) Agricultural Research Service.</p>

Tags: ars, data-gov, digital-image, long-term-monitoring-sites, ltar, new-mexico, np216, phenocam, phenology, primary-productivity
Organization: Department of Agriculture
Data from: Evidence for polyphosphate accumulating organism (PAO)-mediated phosphorus cycling in stream biofilms under alternating aerobic/anaerobic conditions

<p>Phosphorus availability often limits primary production in freshwater ecosystems and excessive P inputs promote accelerated eutrophication. Microbial mechanisms may control O2-dependent uptake/release of P in stream sediments and biofilms, but specific organisms responsible for these cycles have not been identified. Polyphosphate Accumulating Organisms (PAOs) are purposely enriched in treatment plants to remove P from wastewater. PAOs release P under anaerobic conditions and take it up under aerobic conditions. It is hypothesized that alternating aerobic/anaerobic conditions promote patterns of P uptake/release similar to those attributed to PAOs in wastewater treatment. Intact, native stream biofilms were collected in the Cascadilla Creek Watershed in Tompkins County, New York, and subjected to laboratory treatments to impose conditions similar to what may occur because of diel oxygenic and respiratory cycles: 1) continuous sparging with air and 2) alternate sparging with air or anaerobic gas (20∶80% by volume CO2∶N2).  PO43−, Ca, Mg, total Mn, K, Fe2+, and total S (TS) concentrations in the water were monitored and total P (TP) and polyphosphate (polyP) concentrations in the biofilms at the start and end of the experiment. Microscopy and polymerase chain reaction (PCR) were used to quantify the percentage of cells with stored intracellular polyP and to test for known PAO genes, respectively. </p>
<p>This dataset comprises the data, analysis scripts, and visualization scripts for the paper by Saia et al. entitled 'Evidence for polyphosphate accumulating organism (PAO)-mediated phosphorus cycling in stream biofilms under alternating aerobic/anaerobic conditions. Complete dataset description including information on associated journal article, data, and data analysis R scripts can be found at <a href="https://github.com/sheilasaia/paper-p-cycling-in-stream-biofilms/tree/v1.0_for_publication">https://github.com/sheilasaia/paper-p-cycling-in-stream-biofilms/tree/v1.0_for_publication</a>. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Zenodo data package: sheilasaia/paper-p-cycling-in-stream-biofilms: First Public Release.</p> <p>File Name: Web Page, url: <a href="https://doi.org/10.5281/zenodo.242599">https://doi.org/10.5281/zenodo.242599</a> </p><p>This is the first public release of data, analysis scripts, and visualization scripts for the paper by Saia et al. entitled 'Evidence for polyphosphate accumulating organism (PAO)-mediated phosphorus cycling in stream biofilms under alternating aerobic/anaerobic conditions' published in Freshwater Science.</p></li></ul><p></p>

Tags: ars, biogeochemistry, cascadilla-creek, data-gov, phosphorus, polyphosphate-accumulating-organisms, stream-biofilms
Organization: Department of Agriculture
PhenoCam images from JERSHRUBLAND site, Jornada Experimental Range, New Mexico, USA since 2019

<p>This data set consists of repeat digital imagery from the tower-mounted digital cameras (hereafter, PhenoCams) at the Jornada Experimental Range. JER is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes. The archived images provide a permanent record that can be visually-inspected to determine the phenological state of the vegetation at any point in time. Vegetation greenness metrics (e.g., GCC) derived from the ratio of the green color band to sum of red, green, and blue color bands serve as proxies for vegetation greenness.  Greenness metrics can be extracted from the images using simple image processing methods in 1-day or 3-day increments.</p>
<p>This dataset is available to the public and may be freely downloaded. Please keep the designated Contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement.</p>
<p>The development of PhenoCam has been supported by multiple entities. Those include the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (award EF-1065029), DOE’s Regional and Global Climate Modeling program (award DE-SC0016011), the US National Park Service Inventory and Monitoring Program, the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey), and is currently supported by the Department of Agriculture (USDA) Agricultural Research Service.</p>
<p>This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR and the Jornada Experimental Range are supported by the United States Department of Agriculture (USDA) Agricultural Research Service.</p>

Tags: ars, data-gov, digital-image, long-term-monitoring-sites, ltar, new-mexico, np216, phenocam, phenology, primary-productivity
Organization: Department of Agriculture