Host/microbiome interactions in NIH-Heterogeneous Stock rats (study based on 16S data) by Amelie Baud (10967214)

“…</p><p dir="ltr">Includes abundance and taxonomy for all ASVs identified in the samples</p><p dir="ltr"><b>taxonomy_Greengenes2.txt</b></p><p dir="ltr">1) downloaded 2022.10.taxonomy.asv.nwk.qza from http://greengenes.microbio.me/greengenes_release/2022.10-rc1/;</p><p dir="ltr">2) downloaded file 175568_feature-table.qza from Qiita (https://qiita.ucsd.edu/) artefact 175568 in analysis 57950 (same information as BIOM table above)</p><p dir="ltr">3) filtered the taxonomy file to keep only the ASVs present in the HS samples using ```qiime greengenes2 taxonomy-from-table --i-reference-taxonomy 022.10.taxonomy.asv.nwk.qza --i-table 175568_feature-table.qza --o-classification biom.taxonomy.qza```</p><p dir="ltr">4) exported taxonomy to TSV file ```qiime tools export --input-path biom.taxonomy.qza --output-path taxonomy.tsv```</p><p dir="ltr">5) renamed taxonomy as taxonomy_Greengenes2.txt</p><p dir="ltr">Col1 is ASV sequennce</p><p dir="ltr">Col2 is full taxonomy</p><p dir="ltr">Col3 and Col4 not used.…”

Impacts of winter climate change on northern forest understory carbon dioxide exchange determined by reindeer grazing by Noora Kantola (14263490)

“…In addition, ungulate grazers such as reindeer and caribou often alter plant and soil properties that may lead to modifications in the magnitudes and patterns of CO₂ exchange. …”

Additional file 1 of Signatures in domesticated beet genomes pointing at genes under selection in a sucrose-storing root crop by Amar Singh Dhiman (22388349)

“…Leaf beet accessions are grouped into GP1 (biennial leaf beet) and GP2 (annual leaf beet) based on phenotypic classification. Although the grouping is phenotype-based, the two groups also exhibited differing levels of sugar beet and Mediterranean sea beet ancestry at the admixture level. …”

Land cover and climate change as drivers of bird species abundance in South Africa by Christiaan Geldenhuys (22207792)

“…</p><p><br></p><p dir="ltr">Key metadata</p><p dir="ltr">Title: ReportingRates.csv</p><p dir="ltr">Geographic coverage: South Africa (211 SABAP2 pentads selected; pentad = 5′ × 5′ grid cell ≈ 61 km²)</p><p dir="ltr">Spatial resolution: Pentad level (~61 km²)</p><p dir="ltr">Temporal coverage: 2008–2020 (inclusive)</p><p dir="ltr">Number of pentads used: 211</p><p dir="ltr">Primary data sources / provenance: SABAP2 full-protocol checklists; ESA CCI Land Cover (300 m; 2008–2020); TerraClimate (~4 km; 2008–2020)</p><p dir="ltr">Inclusion criteria: Pentads with ≥4 full-protocol SABAP2 checklists per year (2008–2020)</p><p dir="ltr">File format: CSV (comma-separated)</p><p dir="ltr">Date created: 10 September 2025</p><p dir="ltr">Concise notes on columns and units</p><p dir="ltr">Land-cover fields (<code>Cropland</code>, <code>Natural_Vegetation</code>, <code>Sparse_veg_bare_ground</code>, <code>Water</code>, <code>Built_up</code>) are expressed as percent cover of the pentad and should sum approximately to 100% (small differences may arise from classification or excluded classes).</p><p dir="ltr"><code>ppt_mean</code> = annual precipitation total (millimetres).…”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Continuous change detection and classification of land cover using all available Landsat data. …”

A treatise unveiling how all known physics emerges. This work details a revolutionary unification, demonstrating how Quantum Mechanics, the Standard Model, and General Relativity a... by Anthony Jordon (20347548)

“…</li></ul></li><li><b>Falsifiable Predictions:</b> GUHCT makes specific, quantitative predictions across various domains, including potential Lorentz invariance violations at high energies, modifications to gravitational wave dispersion, quantum gravitational decoherence, and distinctive branching ratios for proton decay, offering concrete avenues for experimental falsification.…”

Nocturnal vocalization behavior and related behavioral data of wild tibetan macaques by Gaoxin Gao (22818563)

“…</p><p dir="ltr">Frequency: Frequency (单位：次数)</p><p dir="ltr">含义: Meaning or description of the behavior (in Chinese)</p><p dir="ltr">Type: Behavior type classification (e.g., Social-related, Aggression-related)</p><p><br></p><p dir="ltr">File: 发声时间与种类.xlsx</p><p dir="ltr">Description: The 发声时间与种类.xlsx file contains the following columns in the first sheet:</p><p dir="ltr">Date: The date when the sound event occurred.…”

HU-induced ER expansion is reversible and mimicked by thiol stress. by Ana Sánchez-Molina (22648139)

“…<b>(G)</b> Images showing a timelapse of the cellular architecture modification after DIA exposure. The magenta arrow marks N-Cap apparition; the green arrow marks the apparition of cortical ER expansions. …”

Image dataset: Type series of the new crab-associated leech species<i> Paraclepsis dongnaiensis</i> Bolotov, Eliseeva & Kondakov, 2025 from Vietnam (Hirudinida: Glossiphoniidae) by Ivan Bolotov (11150274)

“…This species is described in our paper entitled “Arthropod-associated leeches (Annelida: Hirudinida) of the World: Diversity, taxonomic reappraisal, ecological classification of host associations, and convergent evolution” (<i>Ecologica Montenegrina </i><b>89</b>: 38-87 (2025); https://doi.org/10.37828/em.2025.89.3).…”

BEF_Coextinctions_Repo by Tom Williams (20453759)

“…</p><h2>Repository Structure</h2><h3> Data Files</h3><h4>Raw Data</h4><ul><li><code><strong>ChAOS_2018_macrofauna_data.csv</strong></code> - Original macrofaunal survey data from the ChAOS 2018 sampling campaign</li><li><ul><li><b>Columns</b>: <code>ScientificName_accepted</code>, <code>Mi</code> (mobility trait, 1-4 scale), <code>Ri</code> (reworking trait, 1-5 scale), <code>Source</code> (trait classification source), <code>Link</code>, <code>Year</code> (sampling year), <code>Station</code>, <code>Replicate</code>, <code>Abundance</code> (total abundance), <code>Biomass</code> (total biomass in grams)</li><li>Contains species-level abundance and biomass with functional traits</li><li>Used in: <code>CoExt_ChAOS_SupplementaryCode.Rmd</code></li></ul></li></ul><h4>Processed Data</h4><ul><li><code><strong>ChAOS_2018_macrofauna_model_ready.csv</strong></code> - Model-ready macrofaunal dataset with extinction scenarios</li><li><ul><li><b>Columns</b>: <code>ScientificName_accepted</code>, <code>Habitat</code> (Arctic/Boreal region), <code>Station</code> (pre-extinction station), <code>Scenario</code> (extinction scenario, e.g., B17-B16), <code>Mi</code>, <code>Ri</code>, <code>Bi</code> (mean individual body size in grams), <code>Ai</code> (mean abundance at pre-extinction station), <code>Btot</code> (total biomass at pre-extinction station), <code>Atot</code> (total abundance at pre-extinction station), <code>Bind_Habitat</code> (mean body size within habitat), <code>Bind_Scenario</code> (mean body size across scenario stations), <code>B_Vulnerability</code> (vulnerability rank score based on Table S2), <code>Amed</code> (median abundance in post-extinction regional pool)</li><li><b>B_Vulnerability</b>: Ranked vulnerabilities to climate-driven transitions based on percentage biomass differences between pre-extinction (northernmost) and post-extinction (southernmost) stations for regional species pool (n=113). …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…Aliquots containing approximately 5 μg of protein were processed using the SP3 protocol as described (89) with some modifications. Briefly, the sample aliquots were brought to 50 μL volume, and disulfide bonds were reduced and alkylated simultaneously with 10 mM TCEP, 40 mM 2-chloroacetamide in 50 mM HEPES pH 8.5, and 1% sodium deoxycholate at 70 °C for 10 minutes, then cooled on ice. …”

<b>Figure 1; 2; 3; 4; 5; 6</b><b>Table 1, </b> 2, 3<b>; </b><b>Sup Table</b> 1,2,3,4; <b>Sup Figure</b> 1,2 by Ramachandran Chelliah (3837415)

“…</b><b> </b>Overview of brown rice-derived bioactive peptide modification and its impact on gut–brain axis regulation. …”

<b>Planning the Incorporation of Vegetation in Cities with Sloped Streets: Efficient Irrigation Using Air-Conditioning Condensate Water and the Creation of Urban Cool Corridors</b>... by Ricardo Riveros (22484566)

“…Identification of Sloped Areas</b></h3><ul><li>Use of digital elevation models (DEM).</li><li>Slope classification: mild (3–5%), medium (6–12%), steep (>13%).…”

Data from: Odor Preference, Feeding, Survival, and Reproductive Fitness of the Invasive Larger Grain Borer Prostephanus truncatus (Horn) on Acorns of Three Native North America Oak... by QINGLEI MING (21614345)

“…</p><p dir="ltr">Headspace solid phase microextraction and gas chromatography-mass spectrometry (HS-SPME/GC-MS) analysis of acorns and grains </p><p dir="ltr">Volatile organic compounds (VOCs) from acorns and grains were analyzed by HS-SPME/GC-MS according to previously developed methodologies (Buśko et al. 2016; De Flaviis et al. 2021) with some modifications, as described below. Maize, wheat, and acorns of bur oak, black oak and red oak were ground separately with a dedicated coffee grinder (Hamilton Beach, Model 80335G, Glen Allen, VA, USA) into a fine whole flour. …”

<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b> by Fan Wang (22125886)

“…</li><li>Quality assurance: The entire dataset underwent a full BIDS validation following these modifications to confirm compliance with the BIDS standard.…”

OVEP code by Yuanfang Guan (22258765)

“…</p><p><br></p><p><br></p><p>---</p><p><br></p><p dir="ltr">## Directory structure (top-level)</p><p><br></p><p dir="ltr">- `rate_code/` </p><p>---</p><p><br></p><p dir="ltr">## Key scripts </p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_maskfunctional.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category maskfunctional.py</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/deep_learn_raw_seq/for_prediction_train_nn_model.py` </p><p dir="ltr"> Arg 'input_features' may take 1 of 3 possible values: - stuctured: using only structured features as input - sequence: using only sequence features as input - both: using both structured and sequence features as inputs</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/variant_classification/plot_gb_feature_importance.py` </p><p dir="ltr"> Script related to: plot gb feature importance.py</p><p dir="ltr">- `rate_code/external_test/calculate_AUC_dataset1_masksnp.py` </p><p dir="ltr"> Script related to: calculate AUC dataset1 masksnp.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category_new.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category new.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional_maskrepeat/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_nolabel.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category nolabel.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis_masklabel/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/plot/plot_indel_snp_auc.py` </p><p dir="ltr"> !…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

<b>Computational Simulation of Combined Droplet Release Systems from Ships and Solar Airships to Enhance Precipitation in Humid Regions: Pilot Case Amazon River and Replicable Zone... by Ricardo Riveros (22484566)

“…</p><h2><b>Scientific Rationale:</b></h2><ul><li>Precipitation modification using <b>artificial cloud condensation nuclei (CCN)</b> has been extensively studied; releasing microdroplets and electrically charged particles can accelerate coalescence and promote rainfall formation.…”

<b>Cooling and Thermal Attenuation System in Vehicles Using Air-Conditioning Condensate Water</b> / <b>Sistema de Enfriamiento y Atenuación Térmica en Vehículos mediante Aprovecham... by Ricardo Riveros (22484566)

“…Feasibility</b></h2><p dir="ltr">The system is compatible with most vehicles equipped with air conditioning and does not require deep modifications.<br>It can produce meaningful results especially in cities with:</p><p dir="ltr"><br></p><ul><li>Extreme temperatures</li><li>Heat-absorbing urban materials (black rock, basalt, hot asphalt)</li><li>Intense and slow traffic</li><li>Low vegetation coverage</li></ul><h2><b>6. …”

Data Sheet 1_What you see is not what you get anymore: a mixed-methods approach on human perception of AI-generated images.pdf by Malte Högemann (22644164)

“…A total of 104 participants took part in an online survey, classifying 50 images (25 real, 25 AI-generated) from five leading TTI models. Alongside their classifications, participants rated their level of confidence and provided optional justifications for their choices. …”