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anticloud-pypsa

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train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
failed_ids: list<item: null>
  child 0, item: null
python: string
total: int64
version: string
sbom_written: bool
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
  child 0, aggregate_rule: string
  child 1, excluded: string
  child 2, reproduce: string
  child 3, thresholds_note: string
platform: string
root: string
failed: int64
schema: string
all_passed: bool
passed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
  child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
      child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
          child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
              child 0, max_files: int64
              child 1, max_lines: int64
              child 2, min_lines: int64
          child 1, code_lines: int64
          child 2, command: string
          child 3, counts_command: string
          child 4, files_source: int64
          child 5, files_tests: int64
          child 6, files_total: int64
          child 7, method: string
          child 8, test_to_source_line_ratio: double
        
...
ail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
          child 62, criteria_missing: list<item: null>
              child 0, item: null
          child 63, criteria_satisfied: int64
          child 64, criteria_total: int64
          child 65, framework_controls_verified: int64
          child 66, ladder: string
          child 67, observed_level: int64
          child 68, source: string
          child 69, trL9_claimed: bool
          child 70, trL9_reason: string
      child 1, id: string
      child 2, ok: bool
      child 3, status: string
      child 4, title: string
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
  child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
      child 0, head: string
      child 1, url: string
      child 2, branch: string
      child 3, sha_verified_from: string
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              digest: string
              kind: string
              path: string
              payload_sha3: string
              prev: string
              seq: int64
              ts: timestamp[s]
              failed_ids: list<item: null>
                child 0, item: null
              python: string
              total: int64
              version: string
              sbom_written: bool
              method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
                child 0, aggregate_rule: string
                child 1, excluded: string
                child 2, reproduce: string
                child 3, thresholds_note: string
              platform: string
              root: string
              failed: int64
              schema: string
              all_passed: bool
              passed: int64
              benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
                child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
                    child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
                        child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
                            child 0, max_files: int64
                            child 1, max_lines: int64
                            child 2, min_lines: int64
                        child 1, code_lines: int64
                        child 2, command: string
                        child 3, counts_command: string
                        child 4, files_source: int64
                        child 5, files_tests: int64
                        child 6, files_total: int64
                        child 7, method: string
                        child 8, test_to_source_line_ratio: double
                      
              ...
              ail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                        child 62, criteria_missing: list<item: null>
                            child 0, item: null
                        child 63, criteria_satisfied: int64
                        child 64, criteria_total: int64
                        child 65, framework_controls_verified: int64
                        child 66, ladder: string
                        child 67, observed_level: int64
                        child 68, source: string
                        child 69, trL9_claimed: bool
                        child 70, trL9_reason: string
                    child 1, id: string
                    child 2, ok: bool
                    child 3, status: string
                    child 4, title: string
              source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
                child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
                    child 0, head: string
                    child 1, url: string
                    child 2, branch: string
                    child 3, sha_verified_from: string
              to
              {'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
              ...
              Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
              because column names don't match

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Check out the documentation for more information.

PYPSA

Governed Anticloud packaging of the upstream project PYPSA in category ELECTRICITY_MANAGEMENT. 16/16 checks PASS (measured). Every number below traces to a named file + run stamp; nothing is borrowed from other projects.

Upstream: PYPSA · Upstream pin: ae2ce7f73dea3c86502f64155af7859c213f9bf5 · Category: ELECTRICITY_MANAGEMENT · Vendor: Anticloud FZ LLE · Licence: MIT

What This Project Does

PyPSA - Python for Power System Analysis

PyPSA stands for Python for Power System Analysis. It is pronounced pipes-ah.

PyPSA is an open-source Python framework for optimising and simulating modern power and energy systems that include features such as conventional generators with unit commitment, variable wind and solar generation, hydro-electricity, inter-temporal storage, coupling to other energy sectors, elastic demands, and linearised power flow with loss approximations in DC and AC networks. PyPSA is designed to scale well with large networks and long time series. It is made for researchers, planners and utilities with basic coding aptitude who need a fast, easy-to-use and transparent tool for power and energy system analysis.

PyPSA has many contributors, with the maintenance led by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin. The project is currently supported by the German Research Foundation (grant number 528775426). Previous versions were developed at the Karlsruhe Institute of Technology funded by the Helmholtz Association, and at FIAS funded by the German Federal Ministry for Education and Research (BMBF).

Features

Economic Dispatch (ED): Models short-term market-based dispatch including unit commitment, renewable availability, short-duration and seasonal storage including hydro reservoirs with inflow and spillage dynamics, elastic demands, load shedding and conversion between energy carriers, using either perfect operational foresight or rolling horizon time resolution.
Linear Optimal Power Flow (LOPF): Extends economic dispatch to determine the least-cost dispatch while respecting network constraints in meshed AC-DC networks, using a linearised representation of power flow (KVL, KCL) with optional loss approximations.
Security-Constrained LOPF (SCLOPF): Extends LOPF by accounting for line outage contingencies to ensure system reliability under $N-1$ conditions.
Capacity Expansion Planning (CEP): Supports least-cost long-term system planning with investment decisions for generation, storage, conversion, and transmission infrastructure. Handles both single and multiple investment periods. Continuous and discrete investments are supported.
Pathway Planning: Supports co-optimisation of multiple investment periods to plan energy system transitions over time with perfect planning foresight.
Stochastic Optimisation: Implements two-stage stochastic programming framework with scenario-weighted uncertain inputs, with investments as first-stage decisions and dispatch as recourse decisions.
Modelling-to-Generate-Alternatives (MGA): Explores near-optimal decision spaces to provide insight into the range of feasible system configurations with similar costs.
Sector-Coupling: Modelling integrated energy systems with multiple energy carriers (electricity, heat, hydrogen, etc.) and conversion between them. Flexible representation of technologies such as heat pumps, electrolysers, battery electric vehicles (BEVs), direct air capture (DAC), and synthetic fuels production.
Static Power Flow Analysis: Computes both full non-linear and linearised load flows for meshed AC and DC grids using Newton-Raphson method.

Documentation

PyPSA has extensive documentation with tutorials, user guides, examples and an API reference.

Installation

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

uv:

uv add pypsa

Usage

import pypsa

# create a new network
n = pypsa.Network()
n.add("Bus", "mybus")
n.add("Load", "myload", bus="mybus", p_set=100)
n.add("Generator", "mygen", bus="mybus", p_nom=100, marginal_cost=20)

# load an example network
n = pypsa.examples.ac_dc_meshed()

# run the optimisation
n.optimize()

# plot results
n.generators_t.p.plot()
n.plot()

# get statistics
n.statistics()
n.statistics.energy_balance()

Dependencies

PyPSA relies heavily on other open-source Python packages. Some of them are:

pandas for storing data about components and time series
numpy and scipy for linear algebra and matrix calculations
linopy for preparing optimisation problems
matplotlib, seaborn and plotly for static and interactive plotting
networkx for network calculations
pytest for unit testing

Find the full list of dependencies in the pyproject.toml file.

PyPSA can be used with different solvers. For instance, the free solvers HiGHS (installed by default), GLPK and CBC or commercial solvers like Gurobi or FICO Xpress for which free academic licenses are available.

Contributing and Support

We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.

To discuss with other PyPSA users, organise projects, share news, and get in touch with the community you can use the Discord server.
For bugs and feature requests, please open an issue.
For troubleshooting and support, please check the troubleshooting and support sections in the documentation.

Detailed guidelines can be found in the Contributing guidelines of our documentation.

Code of Conduct

Please respect our Code of Conduct.

Citing PyPSA

If you use PyPSA for your research, we would appreciate it if you would cite the following paper:

T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, 2018, Journal of Open Research Software, 6(1), arXiv:1707.09913, DOI:10.5334/jors.188

Please use the following BibTeX:

@article{PyPSA,
   author = {T. Brown and J. H\"orsch and D. Schlachtberger},
   title = {{PyPSA: Python for Power System Analysis}},
   journal = {Journal of Open Research Software},
   volume = {6},
   issue = {1},
   number = {4},
   year = {2018},
   eprint = {1707.09913},
   url = {https://doi.org/10.5334/jors.188},
   doi = {10.5334/jors.188}
}

If you want to cite a specific PyPSA version, each release of PyPSA is archived on Zenodo with a release-specific DOI:

Licence

PyPSA is licensed under the open source MIT License. The documentation is licensed under CC-BY-4.0.

The project uses REUSE to expose the licenses of its files.

Installation

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

uv:

uv add pypsa

Usage

PyPSA - Python for Power System Analysis

PyPSA stands for Python for Power System Analysis. It is pronounced pipes-ah.

PyPSA has many contributors, with the maintenance led by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin. The project is currently supported by the German Research Foundation (grant number 528775426). Previous versions were developed at the Karlsruhe Institute of Technology funded by the Helmholtz Association, and at FIAS funded by the German Federal Ministry for Education and Research (BMBF).

API

PyPSA stands for Python for Power System Analysis. It is pronounced pipes-ah.

PyPSA has many contributors, with the maintenance led by the Department of Digital Transformation in Energy Systems at the Technical University of Berlin. The project is currently supported by the German Research Foundation (grant number 528775426). Previous versions were developed at the Karlsruhe Institute of Technology funded by the Helmholtz Association, and at FIAS funded by the German Federal Ministry for Education and Research (BMBF).

Dependencies

Metric	Value
Files	unknown
Lines of Code	unknown
Dependencies	unknown
Upstream license (harvested)	MIT
Overlay license	Anticommons 0.1.0

Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.

Configuration

similar costs.

Sector-Coupling: Modelling integrated energy systems with multiple energy carriers (electricity, heat, hydrogen, etc.) and conversion between them. Flexible representation of technologies such as heat pumps, electrolysers, battery electric vehicles (BEVs), direct air capture (DAC), and synthetic fuels production.
Static Power Flow Analysis: Computes both full non-linear and linearised load flows for meshed AC and DC grids using Newton-Raphson method.

Contributing

🎉 Thank you for your interest in contributing to PyPSA! 🎉

We strongly welcome anyone interested in contributing to PyPSA and its related projects. All types of contributions are encouraged and valued!

For detailed instructions on how to contribute, please refer to the Contributing section of our documentation. This includes guidelines for code contributions, documentation improvements, and example submissions.

To handle the influx of AI-assisted contributions, we have established some basic principles. See the AI-based Contributions section in our documentation for more details.

License

Upstream © its respective contributors under MIT (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.

Upstream

project: PYPSA
Pinned SHA: ae2ce7f73dea3c86502f64155af7859c213f9bf5
source: UPSTREAM_CLONE/ (pinned at the SHA above)
Upstream README source: UPSTREAM_CLONE/README.md

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 2faa66b81f8a5bb8177286c04da3c1a987d1baeb6b80cc7567d054b6a314b3f1.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.

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Total file size:

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Paper for Anticloud/anticloud-pypsa

PyPSA: Python for Power System Analysis

Paper • 1707.09913 • Published Jan 17, 2018