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siegelz
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core-bench

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 153, in _generate_tables
                  df = pd.read_json(f, dtype_backend="pyarrow")
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8c in position 0: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1997, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 156, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 130, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2040, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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field string	language string	capsule_title string	capsule_id string	task_prompt string	results list	capsule_doi string
Computer Science	Python	K-Core based Temporal Graph Convolutional Network for Dynamic Graphs	capsule-7038571	Run the main.py file three times. First, with config/uci.json, the preprocessing task, and the CTGCN-C method. Second, with config/uci.json, the embedding task, and the CTGCN-C method. Third, using python3 with config/uci.json and the link-pred task.	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.9707317.v1
Social Sciences	R	Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: An observational study	capsule-3137115	Run the manuscript.Rmd file using Rscript and render it as html. Put the results in the "../results" folder.	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.1796004.v3
Computer Science	Python	HyperETA: A Non–Deep Learning Method for Estimated Time of Arrival	capsule-5367566	Run run.ipynb and convert the results to html.	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.3533137.v1
Medical Sciences	R	Research Ethics Committees as an intervention point to promote a priori sample size calculations	capsule-9168639	Run the analysis.Rmd file using Rscript and output the results in the 'results' directory.	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.0124369.v1
Computer Science	Python	Synthetic Electrocardiogram Attack Method	capsule-9166182	Run 'Synthetic Electrocardiogram Attack Method.ipynb' and convert the results file to 'html'	[ { "For experiment 1, report the adversary errors with SEAM.": 17, "For experiment 1, report the adversary errors without SEAM.": 58, "For experiment 2, report the adversary errors with SEAM.": 21, "For experiment 2, report the adversary errors without SEAM.": 27, "For experiment 3, report the ad...	https://doi.org/10.1109/jsen.2021.3079177
Medical Sciences	R	Identifying Predictors of Within-person Variance in MRI-based Brain Volume estimates	capsule-0325493	Run 'main.R' using Rscript	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.3688518.v1
Medical Sciences	Python	An Attention-based CNN-BiLSTM Hybrid Neural Network Enhanced with Features of Discrete Wavelet Transformation for Fetal Acidosis Classification	capsule-1854976	Run the 'evaluation.py' file.	[ { "For experiment 1, report the adversary errors with SEAM.": null, "For experiment 1, report the adversary errors without SEAM.": null, "For experiment 2, report the adversary errors with SEAM.": null, "For experiment 2, report the adversary errors without SEAM.": null, "For experiment 3, repor...	https://doi.org/10.24433/CO.4834924.v1
Computer Science	R	Development of an Internet of Things Solution to Monitor and Analyse Indoor Air Quality	capsule-9022937	Run 'IAQ-PostCollection-Analysis.R' using Rscript.	[{"For experiment 1, report the adversary errors with SEAM.":null,"For experiment 1, report the adve(...TRUNCATED)	https://doi.org/10.24433/CO.2005560.v1
Computer Science	Python	Low-Latency Live Video Streaming over a Low-Earth-Orbit Satellite Network with DASH	capsule-8197429	Run 'plot.sh'.	[{"For experiment 1, report the adversary errors with SEAM.":null,"For experiment 1, report the adve(...TRUNCATED)	https://doi.org/10.24433/CO.7355266.v1
Social Sciences	R	"Example of compute capsule for the book chapter \"Developing and Disseminating Data Analysis Tools (...TRUNCATED)	capsule-2916503	Run 'code.R' using Rscript	[{"For experiment 1, report the adversary errors with SEAM.":null,"For experiment 1, report the adve(...TRUNCATED)	https://doi.org/10.24433/CO.8235849.v1

End of preview.

Dataset Card for CORE-Bench

CORE-Bench is a benchmark evaluating the ability of agents to computationally reproduce scientific papers. It comprises 270 tasks from 90 papers across computer science, social science, and medicine, written in Python or R.

Each task in CORE-Bench requires an agent to reproduce the results of a research paper given its repository. The agent must install libraries, packages, and dependencies and run the code. If the code runs successfully, the agent needs to search through all outputs to answer the task questions. The agent submits a report and is evaluated against the results of a successful reproduction. An agent successfully completes a task if it correctly answers all questions about a code repository.

Dataset Details

The benchmark is defined in two files: core_train.json and core_test.json (decrypt the test set using gpg --output core_test.json --decrypt core_test.json.gpg).

Each task in the dataset contains the following fields: field, language, capsule_title, capsule_id, task_prompt, results, and capsule_doi. The files for each environment are themselves not hosted here. The harness automatically downloads the repositories for each task based on the capsule_id from our servers.

Note that the dataset JSON files found in this repository contains the task prompts, task questions, and some other metadata for each task, but not the associated code repositories. The code repositories, which the harness automatically downloads for each task, can be found at https://corebench.cs.princeton.edu/capsules/capsule-XXXXXXX.tar.gz, where XXXXXXX is the capsule_id.

Citation

Dataset Card Authors

Zachary S. Siegel (zss@princeton.edu), Sayash Kapoor (sayashk@princeton.edu), Nitya Nagdir (nn7887@princeton.edu), Benedikt Stroebl (stroebl@princeton.edu), Arvind Narayanan (arvindn@cs.princeton.edu)

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Paper for siegelz/core-bench

CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark

Paper • 2409.11363 • Published Sep 17, 2024 • 2