Data files accompanying our PLoS One publication by Peter Hinow (21810605)

“…The videos were digitized and the positional data were saved in .xlsx or .csv format, respectively. The python codes contain the numerical implementations of our mathematical models.…”

DeepB3Pred_source_code by Muhammad Arif (17012472)

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…</p><p dir="ltr">GPU：NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.…”

Probabilistic-QSR-GeoQA by Mohammad Kazemi (19442467)

“…Also we have written Python API for Probcog (ProbCog-API.py) and SparQ reasoners (SparQ-API.py).…”

Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”

Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) by Winston Yap (13771969)

“…</p><p dir="ltr">Each zipped folder consists the following files:</p><ul><li>Graph data - City object nodes (.parquet) and COO format edges (.txt)</li><li>predictions.txt (model predictions from GraphSAGE model)</li><li>final_energy.parquet (Compiled training and validation building energy data)</li></ul><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

Leue Modulation Coefficients (LMC): A Smooth Continuum Embedding of Bounded Arithmetic Data by Jeanette Leue (22470409)

“…This Zenodo package includes: the full research paper (PDF), a complete Python implementation generating the LMC field and conductivity model, a numerical plot comparing discrete LMC values with the smoothed continuum field, a cover letter and supporting documentation. …”

Error reduction over time by the HOFA-SMC. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

Comparison of SMC techniques. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

Proposed HOFA-SMC with experimental validation. by Asra Sarwat (22453794)

“…A detailed simulation study is conducted on a full hand model, comprising four 4-degree-of-freedom (DOF) fingers and a 3-DOF thumb, implemented in Python. …”

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation by Hong Zhu (109912)

“…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. This algorithm is implemented in ReaxANA, a parallel Python package designed to extract reaction mechanisms from both heterogeneous and homogeneous reactive MD trajectories. …”

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation by Hong Zhu (109912)

“…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. This algorithm is implemented in ReaxANA, a parallel Python package designed to extract reaction mechanisms from both heterogeneous and homogeneous reactive MD trajectories. …”

Overview of generalized weighted averages. by Nobuhito Manome (8882084)

“…<div><p>The multi-armed bandit (MAB) problem is a classical problem that models sequential decision-making under uncertainty in reinforcement learning. …”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”

A Fully Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture by Nagarajan Kandasamy (8400168)

“…QUANTISENC’s software-defined hardware design methodology allows the user to train an SNN model using Python and evaluate performance of its hardware implementation, such as area, power, latency, and throughput. …”

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

“…As a result, collaborative analysis is restricted, and the goals of open, reproducible, and scalable scientific workflows remain out of reach for many. </p><p dir="ltr">A new model for scientific data management is transforming how large-scale, multidimensional datasets are stored, accessed, and analyzed in the cloud. …”

Globus Compute: Federated FaaS for Integrated Research Solutions by eRNZ Admin (6438486)

“…</p><p dir="ltr">Globus Compute [2] is a Function-as-a-Service platform designed to provide a scalable, secure, and simple interface to HPC resources. Globus Compute implements a federated model via which users may deploy endpoints on arbitrary remote computers, from the edge to high performance computing (HPC) cluster, and they may then invoke Python functions on those endpoints via a reliable cloud-hosted service. …”

Automatic data reduction for the typical astronomer by Bradford Holden (21789524)

“…PypeIt has been developed by a small team of astronomers with two leading philosophies: (1) build instrument-agnostic code to serve nearly any spectrograph; (2) implement algorithms that achieve Poisson-level sky-subtraction with minimal systematics to yield precisely calibrated spectra with a meaningful noise model. …”