Geo-CLIP-For-Street-View 著者: Sheng Hu (20618468)

“…</p><h3>Case study 1: Urban village classification</h3><p><br>```</p><p dir="ltr">CaseStudy1/</p><p dir="ltr">├── data/</p><p dir="ltr">| ├── train_img.npy # the npy file converted from the original street view images of the training set, shape: (n_train, 4, 3, 256, 256)</p><p dir="ltr">| ├── train_feat.npy # the visual knowledge of the training set, shape: (n_train, 128)</p><p dir="ltr">| ├── train_svid.npy # the street view image IDs of the training set, shape: (n_train,)</p><p dir="ltr">| ├── train_y.npy # the label of the training set, shape: (n_train,)</p><p dir="ltr">| ├── val_img.npy # the npy file converted from the original street view images of the validation set, shape: (n_val, 4, 3, 256, 256)</p><p dir="ltr">| ├── val_feat.npy: # the visual knowledge of the validation set, shape: (n_val, 128)</p><p dir="ltr">| ├── val_svid.npy # the street view image IDs of the validation set, shape: (n_val,)</p><p dir="ltr">| ├── val_y.npy # the label of the validation set, shape: (n_val,)</p><p dir="ltr">| ├── dist.npy # the distance matrix, shape: (n, n)</p><p dir="ltr">| ├── svids.npy # the street view image IDs corresponding to the distance matrix, shape: (n,)</p><p dir="ltr"><br></p><p dir="ltr">with:</p><p dir="ltr"> n: the number of samples</p><p dir="ltr"> n_train: the number of training samples</p><p dir="ltr"> n_val: the number of validation samples</p><p dir="ltr"> n_train : n_val = 7 : 3</p><p>```</p><h3><br>Case study 2: Urban mobility pattern prediction</h3><p><br>``` </p><p dir="ltr">CaseStudy2/</p><p dir="ltr">├── data/ </p><p dir="ltr">| ├── pretrain/</p><p dir="ltr">| ├── train_img.npy # the npy file converted from the original street view images of the training set, shape: (n_train, 3, 256, 256)</p><p dir="ltr">| ├── train_feat.npy # the visual knowledge of the training set, shape: (n_train, 30)</p><p dir="ltr">| ├── train_svid.npy # the street view image IDs of the training set, shape: (n_train,)</p><p dir="ltr">| ├── val_img.npy # the npy file converted from the original street view images of the validation set, shape: (n_val, 3, 256, 256)</p><p dir="ltr">| ├── val_feat.npy: # the visual knowledge of the validation set, shape: (n_val, 30)</p><p dir="ltr">| ├── val_svid.npy # the street view image IDs of the validation set, shape: (n_val,)</p><p dir="ltr">| ├── dist.npy # the distance matrix, shape: (n, n)</p><p dir="ltr">| ├── svids.npy # the street view image IDs corresponding to the distance matrix, shape: (n,)</p><p dir="ltr">| ├── train/</p><p dir="ltr">| ├── feats/ </p><p dir="ltr">| ├── 1.npy # the npy file represents the visual knowledge corresponding to the region with ID 1， shape (num_SVI_1, 30)</p><p dir="ltr">| ├── 2.npy # the npy file represents the visual knowledge corresponding to the region with ID 2， shape (num_SVI_2, 30)</p><p dir="ltr">| ├── 3.npy # the npy file represents the visual knowledge corresponding to the region with ID 3， shape (num_SVI_3, 30)</p><p dir="ltr">| ├── imgs/ </p><p dir="ltr">| ├── 1.npy # the npy file represents the street view imagery corresponding to the region with ID 1， shape (num_SVI_1, 3, 256, 256)</p><p dir="ltr">| ├── 2.npy # the npy file represents the street view imagery corresponding to the region with ID 2， shape (num_SVI_2, 3, 256, 256)</p><p dir="ltr">| ├── 3.npy # the npy file represents the street view imagery corresponding to the region with ID 3， shape (num_SVI_3, 3, 256, 256)</p><p dir="ltr">| ├── fliter/ </p><p dir="ltr">| ├── train_flow.npy # the taxi flow count of the training set, shape: (n_region_train,5)</p><p dir="ltr">| ├── train_id.npy # the regions of the training set, shape: (n_region_train,)</p><p dir="ltr">| ├── val_flow.npy # the taxi flow count of the validation set, shape: (n_region_val,5)</p><p dir="ltr">| ├── val_id.npy # the regions of the validation set, shape: (n_region_val,)</p><p dir="ltr"><br></p><p dir="ltr">with:</p><p dir="ltr"> n: the number of image samples</p><p dir="ltr"> n_train: the number of training samples</p><p dir="ltr"> n_val: the number of validation samples</p><p dir="ltr"> n_train : n_val = 7 : 3</p><p dir="ltr"> n_region_train: the number of regions in the training set</p><p dir="ltr"> n_region_val: the number of regions in the validation set</p><p dir="ltr"> n_region_train : n_region_val = 7 : 3</p><p dir="ltr"><br></p><p dir="ltr"> num_SVI_1: the number of street view images collected in the region with id 1</p><p dir="ltr"> num_SVI_2: the number of street view images collected in the region with id 2</p><p dir="ltr"> num_SVI_3: the number of street view images collected in the region with id 3</p><p dir="ltr"><br></p><p>```</p><p dir="ltr">For detailed descriptions of the data, its format, and how to use it in the experiment, please refer to the <b>README-data.md</b> file.…”

Host/microbiome interactions in NIH-Heterogeneous Stock rats (study based on 16S data) 著者: 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.…”

Additional file 1 of Rotifers in space: transcriptomic response of the bdelloid rotifer Adineta vaga aboard the International Space Station 著者: Victoria C. Moris (17848917)

“…Animals remain active after leak test. No modification of behavior was reported. Captured using Zeiss Stemi 305 Binoccular coupled with Canon camera. …”

Mn-Based Polyoxometalates with Organic–Inorganic Large-Spaced Layers as the Highly Efficient Electrocatalysts for Acidic and Alkaline Hydrogen Evolution Reaction 著者: Meijia Wang (2705005)

“…Organic–inorganic hybridized polyoxometalates are the focus of research in the field of electrocatalytic hydrogen evolution through substitution by transition metal ions or modification by organic ligands. Using a one-step solution method, two new Mn-based polyoxometalates were successfully created, Na₄(C₄H₇N₂)[{Mn₃WO₃}[BiW₉O₃₃]₂(H₃O)₈]·7H₂O (1), Na₇(C₃H₃N₂)[{MnWO₃}{BiW₉O₃₃}]·14H₂O (2). …”

Mn-Based Polyoxometalates with Organic–Inorganic Large-Spaced Layers as the Highly Efficient Electrocatalysts for Acidic and Alkaline Hydrogen Evolution Reaction 著者: Meijia Wang (2705005)

“…Organic–inorganic hybridized polyoxometalates are the focus of research in the field of electrocatalytic hydrogen evolution through substitution by transition metal ions or modification by organic ligands. Using a one-step solution method, two new Mn-based polyoxometalates were successfully created, Na₄(C₄H₇N₂)[{Mn₃WO₃}[BiW₉O₃₃]₂(H₃O)₈]·7H₂O (1), Na₇(C₃H₃N₂)[{MnWO₃}{BiW₉O₃₃}]·14H₂O (2). …”

<b>Factors Associated With Initiation of Sodium–Glucose Cotransporter 2 Inhibitor and Glucagon-Like Peptide 1 Receptor Agonists in Patients With Diabetes and Kidney Disease: A Post... 著者: Linda-Marie U. Lavenburg (22189408)

“…Enrolled patients with type 2 diabetes who were not prescribed an SGLT2 inhibitor or a GLP-1 receptor agonist at baseline were included. Effect modification by K-CHAMP was assessed using interaction terms.…”

Additional file 1 of Signatures in domesticated beet genomes pointing at genes under selection in a sucrose-storing root crop 著者: 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 著者: 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) 著者: U.S. Forest Service (17476914)

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

Platelets and Thrombosis - Boehringer Ingelheim/Persantin 100 著者: Claire Price (6727883)

“…Persantin 100 is therefore indicated wherever modification of platelet funcation may be beneficial. …”

Workflow diagram to assess target product profile of gene drive-modified mosquitoes. 著者: Agastya Mondal (18626010)

“…<p><b>(A)</b> We consider a population modification gene drive system and antimalarial effector gene(s) with defined transmission-blocking efficacy (<i>b</i>_{<b><i>H</i></b>}). …”

Nocturnal vocalization behavior and related behavioral data of wild tibetan macaques 著者: 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.…”

RBM39 recruits YTHDC1 to promote Tat degradation. 著者: Xiaohui Deng (534130)

“…<b>(B)</b> Assessment of Tat m⁶A modification levels through RIP of m⁶A-modified RNA isolated from HEK293T cells transfected with Tat-Flag, RBM39-HA, and YTHDC1-HA plasmids, followed by qRT-PCR analysis. …”

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

<i>Mechanisms of Media Influence and Public Opinion Formation in Contexts of Information Saturation / </i><i>Mecanismos de Influencia Mediática y Formación de Opinión Pública en Co... 著者: Ricardo Riveros (22484566)

“…The Role of Information Saturation</b><p dir="ltr">Information saturation —high volumes of messages, notifications, visual and auditory stimuli— reduces critical analysis capacity. …”

<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>... 著者: 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%).…”

HU-induced ER expansion is reversible and mimicked by thiol stress. 著者: 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. …”

OVEP code 著者: 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 著者: 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). …”