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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'Code', 'RQ', 'Original', 'Q', 'Type', 'Industry', 'Respondent_ID'}) and 10 missing columns ({'I3_Finance', 'Research_Implication', 'I1_Tech', 'Sub_Code', 'I4_Telecom', 'Code_Category', 'Cross_Interview_Pattern', 'I2_Energy', 'I6_Education', 'I5_FMCG'}).
This happened while the csv dataset builder was generating data using
hf://datasets/itseffi/epfl-enterprise-osai-adoption-research-data/EPFL_Survey_Qualitative_Coding.csv (at revision 0a98ccfadf68b00256cd06f602fff6d317a457b7)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Respondent_ID: int64
Industry: string
Q: string
Original: string
Code: string
Type: string
RQ: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1038
to
{'Code_Category': Value('string'), 'Sub_Code': Value('string'), 'I1_Tech': Value('string'), 'I2_Energy': Value('string'), 'I3_Finance': Value('string'), 'I4_Telecom': Value('string'), 'I5_FMCG': Value('string'), 'I6_Education': Value('string'), 'Cross_Interview_Pattern': Value('string'), 'Research_Implication': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, 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 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 7 new columns ({'Code', 'RQ', 'Original', 'Q', 'Type', 'Industry', 'Respondent_ID'}) and 10 missing columns ({'I3_Finance', 'Research_Implication', 'I1_Tech', 'Sub_Code', 'I4_Telecom', 'Code_Category', 'Cross_Interview_Pattern', 'I2_Energy', 'I6_Education', 'I5_FMCG'}).
This happened while the csv dataset builder was generating data using
hf://datasets/itseffi/epfl-enterprise-osai-adoption-research-data/EPFL_Survey_Qualitative_Coding.csv (at revision 0a98ccfadf68b00256cd06f602fff6d317a457b7)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Code_Category
string | Sub_Code
string | I1_Tech
string | I2_Energy
string | I3_Finance
string | I4_Telecom
string | I5_FMCG
string | I6_Education
string | Cross_Interview_Pattern
string | Research_Implication
string |
|---|---|---|---|---|---|---|---|---|---|
Decision_Factors
|
Cost_Performance_Tradeoff
|
Primary Driver
|
Secondary
|
Not Primary
|
Balanced
|
Monitored
|
Secondary
|
Industry-specific cost prioritization
|
RQ2: Decision factors vary by industry risk tolerance
|
Decision_Factors
|
Compliance_Security
|
Secondary
|
Primary
|
Primary
|
Secondary
|
Primary
|
Secondary
|
Regulated industries prioritize compliance
|
RQ2: Risk tolerance drives decision factors
|
Decision_Factors
|
Technical_Suitability
|
Primary
|
Secondary
|
Secondary
|
Primary
|
Primary
|
Primary
|
Technical fit matters across industries
|
RQ2: Technical requirements are universal
|
Integration_Challenges
|
Expertise_Gaps
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal skill gap barrier
|
RQ2: Organizational readiness is critical
|
Integration_Challenges
|
Documentation_Gaps
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal documentation need
|
RQ2: Knowledge transfer barriers exist
|
Integration_Challenges
|
Legacy_Systems
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal technical debt
|
RQ2: Infrastructure challenges are common
|
Motivations
|
Control_Customization
|
Primary
|
Secondary
|
Not Present
|
Primary
|
Secondary
|
Primary
|
Control needs vary by industry
|
RQ2: Motivations align with industry needs
|
Motivations
|
Risk_Aversion
|
Secondary
|
Primary
|
Primary
|
Secondary
|
Primary
|
Secondary
|
Risk tolerance drives adoption
|
RQ2: Risk profile determines adoption path
|
Governance_Gates
|
Licensing_Documentation
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal governance requirement
|
RQ3: Gates are universal prerequisites
|
Governance_Gates
|
Compliance_Certification
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal compliance need
|
RQ3: Compliance gates are mandatory
|
Governance_Gates
|
Provenance_Traceability
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal transparency need
|
RQ3: Transparency is universal requirement
|
Execution_Levers
|
Cost_Optimization
|
Primary
|
Conditional
|
Not Active
|
Primary
|
Primary
|
Secondary
|
Gate-dependent activation
|
RQ3: Levers activate after gates
|
Execution_Levers
|
Performance_Latency
|
Primary
|
Secondary
|
Not Active
|
Primary
|
Secondary
|
Secondary
|
Performance focus varies
|
RQ3: Performance levers are conditional
|
Execution_Levers
|
Operational_Efficiency
|
Secondary
|
Secondary
|
Not Active
|
Secondary
|
Primary
|
Secondary
|
Operational focus varies
|
RQ3: Operational levers depend on gates
|
Gate_Lever_Sequence
|
Sequential_Activation
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal sequential model
|
RQ3: Gates must precede levers
|
Gate_Lever_Sequence
|
Conditional_Levers
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal conditional activation
|
RQ3: Levers depend on gate satisfaction
|
Gate_Lever_Sequence
|
Governance_First
|
Present
|
Present
|
Present
|
Present
|
Present
|
Present
|
Universal governance priority
|
RQ3: Governance-first adoption model
|
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End of preview.
EPFL Enterprise Open-Source AI Adoption Research Dataset
Dataset Summary
This dataset contains mixed-methods research data from 100 organizations regarding their strategic adoption of open-source AI through the Hugging Face ecosystem. The research was conducted at EPFL (École Polytechnique Fédérale de Lausanne) and supports the development of the Gate-Lever framework for enterprise open-source AI adoption.
Dataset Structure
This dataset is organized into 4 configurations to handle different data schemas:
Configuration 1: Survey Data (survey_data)
EPFL_Enterprise_OSAI_Adoption_Survey_Data.csv: Main survey responses from 100 organizations
Configuration 2: Interview Data (interview_data)
EPFL_Enterprise_OSAI_Adoption_Interview_Data.csv: Interview transcripts and analysis from 6 organizations
Configuration 3: Coding Data (coding_data)
EPFL_Coding_Matrix.csv: Gate-Lever coding framework structureEPFL_Survey_Qualitative_Coding.csv: Complete survey coding with adoption stagesEPFL_Qualitative_Coding_Analysis.csv: Coding frequency analysisEPFL_Code_Frequencies_with_RQ.csv: Coding frequencies mapped to research questions
Configuration 4: Supporting Data (supporting_data)
EPFL_Quantitative_Summary_Statistics.csv: Statistical summary tablesEPFL_Method_Appendix_Instrument_RQ_Mapping.csv: Survey questions mapped to research questions
Research Framework
This dataset supports the Gate-Lever Framework for strategic open-source AI adoption:
- Governance Gates: Compliance, data privacy, security, documentation, licensing clarity
- Execution Levers: Performance, cost efficiency, customization, support, time-to-value
- Adoption Stages: Pre-adoption, Adopting, Adopted
- Organizational Clusters: Performance-Driven Adopters, Governance-Locked Organizations, Balanced Transition Organizations
Key Variables
Survey Data Variables
- Respondent_ID: Anonymous respondent identifier
- Org_Size: Organization size category
- Industry: Industry sector (12 categories)
- HF_Familiarity: Hugging Face familiarity (1-5 scale)
- AI_Solution_Type: Current AI solution type (Proprietary/Both/Open-source)
- Decision_Factors: Key decision factors (open text)
- Integration_Difficulty: Integration challenge rating (1-5 scale)
- Adoption_Intention: Plan to increase Hugging Face use (Yes/No/Not sure)
- AI_Stage_Overall: AI maturity stage (Early/Intermediate/Advanced)
- OpenSource_Stage: Open-Source AI Adoption stage (Pre-adoption/Adopting/Adopted)
- Gate_Index: Governance/compliance factors index (0-1)
- Lever_Index: Performance/optimization factors index (0-1)
- Adoption_Intention_Binary: Binary adoption intention (0/1)
- Cluster: Organizational profile cluster (0-2)
Interview Data Variables
- ID: Interview identifier (I1-I6)
- Industry: Participant industry sector
- Role: Participant role/title
- Stage: Adoption stage classification
- Theme 1-6: Coded themes from thematic analysis
- Framework_Link: Connection to Gate-Lever framework
- Main_Takeaway: Key insights from each interview
Methodology
Data Collection
- Survey: Online survey with structured and open-text questions
- Interviews: Semi-structured interviews (30-45 minutes each)
- Coding: Systematic thematic analysis using Gate-Lever framework
- Validation: Inter-coder reliability testing (85% agreement)
Statistical Analysis
- Descriptive Statistics: Sample characteristics and variable distributions
- Correlation Analysis: Gate-Lever relationships and adoption predictors
- Cluster Analysis: K-means clustering for organizational segmentation
- Regression Analysis: Logistic and linear regression for adoption prediction
- ANOVA: Integration difficulty differences across adoption stages
Usage
Loading the Dataset
from datasets import load_dataset
# Load specific configurations
survey_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "survey_data")
interview_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "interview_data")
coding_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "coding_data")
supporting_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "supporting_data")
# Or load all configurations
dataset = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data")
Suitable For
- Open-source AI adoption frameworks
- Strategic AI adoption research
- Enterprise technology adoption studies
- Mixed-methods research validation
- Technology adoption theory development
Limitations
- Sample limited to European organizations
- Self-reported data may introduce bias
- Focus on Hugging Face ecosystem specifically
License
This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Contact
For questions about this dataset, please use the Hugging Face repository discussions.
Repository Information
- Repository: https://huggingface.co/datasets/itseffi/epfl-enterprise-osai-adoption-research-data
- Institution: EPFL (École Polytechnique Fédérale de Lausanne)
- Research Project: Strategic Enterprise Adoption of Hugging Face
- Framework: Enterprise Open-Source AI Adoption Framework
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Repository
epfl-enterprise-osai-adoption-research-data
Institution
: EPFL (École Polytechnique Fédérale de Lausanne)
Research Type
: Mixed-methods (quantitative survey + qualitative interviews)
Sample Size
: 100 organizations + 6 in-depth interviews
Industries
: 12 sectors (Technology, Finance, Healthcare, Energy, etc.)
Collection Period
: 2025
Anonymization
: All identifying information removed
Size of downloaded dataset files:
68.4 kB
Size of the auto-converted Parquet files:
75.2 kB
Number of rows:
162