Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from Demonstration Article Swipe
Yasser Mohammad
,
Toyoaki Nishida
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2015.082
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2015.082
Learning from Demonstration is an important technology for the new wave of robots that are envisioned to work side-by-side with workers in factories as well as social robots. Most available techniques for learning from demonstration rely on the existence of a training set of demonstrations that is assumed to be pre-segmented and is usually processed in batch. Another problem with most available methods is the need to set the model complexity used to model the motion. In this paper, we propose a solution to both problems based on incremental piecewise linear segmentation of the motion using an extension of the SWAB algorithm. Evaluation experiments show that the proposed method is able to generate motion models with adequate complexity without the need for model comparison methods and assuming incremental streaming of demonstrations rather than the availability of the complete training set in advance. The proposed method is applicable not only to robot learning from demonstration but to other industrial applications in which an accurate model of time variant data needs to be built from streaming input.
Related Topics
Concepts
Computer science
Robot
Piecewise linear function
Segmentation
Piecewise
Set (abstract data type)
Motion (physics)
Machine learning
Artificial intelligence
Simple (philosophy)
Unsupervised learning
Computer vision
Mathematics
Philosophy
Programming language
Epistemology
Mathematical analysis
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.12792/iciae2015.082
- https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454
- OA Status
- gold
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2324934267
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2324934267Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.12792/iciae2015.082Digital Object Identifier
- Title
-
Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from DemonstrationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Yasser Mohammad, Toyoaki NishidaList of authors in order
- Landing page
-
https://doi.org/10.12792/iciae2015.082Publisher landing page
- PDF URL
-
https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454Direct OA link when available
- Concepts
-
Computer science, Robot, Piecewise linear function, Segmentation, Piecewise, Set (abstract data type), Motion (physics), Machine learning, Artificial intelligence, Simple (philosophy), Unsupervised learning, Computer vision, Mathematics, Philosophy, Programming language, Epistemology, Mathematical analysis, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2015: 1Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2324934267 |
|---|---|
| doi | https://doi.org/10.12792/iciae2015.082 |
| ids.doi | https://doi.org/10.12792/iciae2015.082 |
| ids.mag | 2324934267 |
| ids.openalex | https://openalex.org/W2324934267 |
| fwci | 0.31660115 |
| type | article |
| title | Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from Demonstration |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 478 |
| biblio.first_page | 471 |
| topics[0].id | https://openalex.org/T10653 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9990000128746033 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2207 |
| topics[0].subfield.display_name | Control and Systems Engineering |
| topics[0].display_name | Robot Manipulation and Learning |
| topics[1].id | https://openalex.org/T11512 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9879999756813049 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Anomaly Detection Techniques and Applications |
| topics[2].id | https://openalex.org/T10876 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9879999756813049 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Fault Detection and Control Systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7451505064964294 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C90509273 |
| concepts[1].level | 2 |
| concepts[1].score | 0.672002375125885 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11012 |
| concepts[1].display_name | Robot |
| concepts[2].id | https://openalex.org/C17095337 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6373793482780457 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2375229 |
| concepts[2].display_name | Piecewise linear function |
| concepts[3].id | https://openalex.org/C89600930 |
| concepts[3].level | 2 |
| concepts[3].score | 0.637344479560852 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[3].display_name | Segmentation |
| concepts[4].id | https://openalex.org/C164660894 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5995187759399414 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2037833 |
| concepts[4].display_name | Piecewise |
| concepts[5].id | https://openalex.org/C177264268 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5992829203605652 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[5].display_name | Set (abstract data type) |
| concepts[6].id | https://openalex.org/C104114177 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5359829664230347 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q79782 |
| concepts[6].display_name | Motion (physics) |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.5352028012275696 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.5256544351577759 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C2780586882 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4707741439342499 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7520643 |
| concepts[9].display_name | Simple (philosophy) |
| concepts[10].id | https://openalex.org/C8038995 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41931843757629395 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[10].display_name | Unsupervised learning |
| concepts[11].id | https://openalex.org/C31972630 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3337521553039551 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[11].display_name | Computer vision |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.13576403260231018 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| concepts[14].id | https://openalex.org/C199360897 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[14].display_name | Programming language |
| concepts[15].id | https://openalex.org/C111472728 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[15].display_name | Epistemology |
| concepts[16].id | https://openalex.org/C134306372 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[16].display_name | Mathematical analysis |
| concepts[17].id | https://openalex.org/C2524010 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[17].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7451505064964294 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/robot |
| keywords[1].score | 0.672002375125885 |
| keywords[1].display_name | Robot |
| keywords[2].id | https://openalex.org/keywords/piecewise-linear-function |
| keywords[2].score | 0.6373793482780457 |
| keywords[2].display_name | Piecewise linear function |
| keywords[3].id | https://openalex.org/keywords/segmentation |
| keywords[3].score | 0.637344479560852 |
| keywords[3].display_name | Segmentation |
| keywords[4].id | https://openalex.org/keywords/piecewise |
| keywords[4].score | 0.5995187759399414 |
| keywords[4].display_name | Piecewise |
| keywords[5].id | https://openalex.org/keywords/set |
| keywords[5].score | 0.5992829203605652 |
| keywords[5].display_name | Set (abstract data type) |
| keywords[6].id | https://openalex.org/keywords/motion |
| keywords[6].score | 0.5359829664230347 |
| keywords[6].display_name | Motion (physics) |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.5352028012275696 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.5256544351577759 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/simple |
| keywords[9].score | 0.4707741439342499 |
| keywords[9].display_name | Simple (philosophy) |
| keywords[10].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[10].score | 0.41931843757629395 |
| keywords[10].display_name | Unsupervised learning |
| keywords[11].id | https://openalex.org/keywords/computer-vision |
| keywords[11].score | 0.3337521553039551 |
| keywords[11].display_name | Computer vision |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.13576403260231018 |
| keywords[12].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.12792/iciae2015.082 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015 |
| locations[0].landing_page_url | https://doi.org/10.12792/iciae2015.082 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5007758273 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2272-7254 |
| authorships[0].author.display_name | Yasser Mohammad |
| authorships[0].countries | EG |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I91041137 |
| authorships[0].affiliations[0].raw_affiliation_string | Assiut University, Egypt |
| authorships[0].institutions[0].id | https://openalex.org/I91041137 |
| authorships[0].institutions[0].ror | https://ror.org/01jaj8n65 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I91041137 |
| authorships[0].institutions[0].country_code | EG |
| authorships[0].institutions[0].display_name | Assiut University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yasser Mohammad |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Assiut University, Egypt |
| authorships[1].author.id | https://openalex.org/A5108427048 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Toyoaki Nishida |
| authorships[1].countries | JP |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I22299242 |
| authorships[1].affiliations[0].raw_affiliation_string | Kyoto University |
| authorships[1].institutions[0].id | https://openalex.org/I22299242 |
| authorships[1].institutions[0].ror | https://ror.org/02kpeqv85 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I22299242 |
| authorships[1].institutions[0].country_code | JP |
| authorships[1].institutions[0].display_name | Kyoto University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Toyoaki Nishida |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Kyoto University |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Simple Incremental GMM Modeling using Multidimensional Piecewise Linear Segmentation for Learning from Demonstration |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10653 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9990000128746033 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2207 |
| primary_topic.subfield.display_name | Control and Systems Engineering |
| primary_topic.display_name | Robot Manipulation and Learning |
| related_works | https://openalex.org/W2385263368, https://openalex.org/W2133947431, https://openalex.org/W2490196280, https://openalex.org/W3182827720, https://openalex.org/W1596838019, https://openalex.org/W3208509670, https://openalex.org/W2109304297, https://openalex.org/W2100864966, https://openalex.org/W2083709218, https://openalex.org/W4323366562 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2015 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.12792/iciae2015.082 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015 |
| best_oa_location.landing_page_url | https://doi.org/10.12792/iciae2015.082 |
| primary_location.id | doi:10.12792/iciae2015.082 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www2.ia-engineers.org/conference/index.php/iciae/iciae2015/paper/download/646/454 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015 |
| primary_location.landing_page_url | https://doi.org/10.12792/iciae2015.082 |
| publication_date | 2015-01-01 |
| publication_year | 2015 |
| referenced_works | https://openalex.org/W6646884813, https://openalex.org/W2044049559, https://openalex.org/W2116039916, https://openalex.org/W2123967136, https://openalex.org/W2129202194, https://openalex.org/W2063346060, https://openalex.org/W1989037929, https://openalex.org/W2161299247, https://openalex.org/W1980979787, https://openalex.org/W2107633943, https://openalex.org/W2090524094, https://openalex.org/W2146121659, https://openalex.org/W2151378685, https://openalex.org/W4242702158, https://openalex.org/W3143045513, https://openalex.org/W2041580073, https://openalex.org/W1986014385, https://openalex.org/W2172131460 |
| referenced_works_count | 18 |
| abstract_inverted_index.a | 40, 81 |
| abstract_inverted_index.In | 76 |
| abstract_inverted_index.an | 4, 96, 161 |
| abstract_inverted_index.as | 23, 25 |
| abstract_inverted_index.be | 49, 170 |
| abstract_inverted_index.in | 21, 55, 140, 159 |
| abstract_inverted_index.is | 3, 46, 52, 63, 109, 145 |
| abstract_inverted_index.of | 11, 39, 43, 92, 98, 129, 135, 164 |
| abstract_inverted_index.on | 36, 87 |
| abstract_inverted_index.to | 16, 48, 66, 72, 83, 111, 149, 155, 169 |
| abstract_inverted_index.we | 79 |
| abstract_inverted_index.The | 142 |
| abstract_inverted_index.and | 51, 125 |
| abstract_inverted_index.are | 14 |
| abstract_inverted_index.but | 154 |
| abstract_inverted_index.for | 7, 31, 121 |
| abstract_inverted_index.new | 9 |
| abstract_inverted_index.not | 147 |
| abstract_inverted_index.set | 42, 67, 139 |
| abstract_inverted_index.the | 8, 37, 64, 68, 74, 93, 99, 106, 119, 133, 136 |
| abstract_inverted_index.Most | 28 |
| abstract_inverted_index.SWAB | 100 |
| abstract_inverted_index.able | 110 |
| abstract_inverted_index.both | 84 |
| abstract_inverted_index.data | 167 |
| abstract_inverted_index.from | 1, 33, 152, 172 |
| abstract_inverted_index.most | 60 |
| abstract_inverted_index.need | 65, 120 |
| abstract_inverted_index.only | 148 |
| abstract_inverted_index.rely | 35 |
| abstract_inverted_index.show | 104 |
| abstract_inverted_index.than | 132 |
| abstract_inverted_index.that | 13, 45, 105 |
| abstract_inverted_index.this | 77 |
| abstract_inverted_index.time | 165 |
| abstract_inverted_index.used | 71 |
| abstract_inverted_index.wave | 10 |
| abstract_inverted_index.well | 24 |
| abstract_inverted_index.with | 19, 59, 115 |
| abstract_inverted_index.work | 17 |
| abstract_inverted_index.based | 86 |
| abstract_inverted_index.built | 171 |
| abstract_inverted_index.model | 69, 73, 122, 163 |
| abstract_inverted_index.needs | 168 |
| abstract_inverted_index.other | 156 |
| abstract_inverted_index.robot | 150 |
| abstract_inverted_index.using | 95 |
| abstract_inverted_index.which | 160 |
| abstract_inverted_index.batch. | 56 |
| abstract_inverted_index.input. | 174 |
| abstract_inverted_index.linear | 90 |
| abstract_inverted_index.method | 108, 144 |
| abstract_inverted_index.models | 114 |
| abstract_inverted_index.motion | 94, 113 |
| abstract_inverted_index.paper, | 78 |
| abstract_inverted_index.rather | 131 |
| abstract_inverted_index.robots | 12 |
| abstract_inverted_index.social | 26 |
| abstract_inverted_index.Another | 57 |
| abstract_inverted_index.assumed | 47 |
| abstract_inverted_index.methods | 62, 124 |
| abstract_inverted_index.motion. | 75 |
| abstract_inverted_index.problem | 58 |
| abstract_inverted_index.propose | 80 |
| abstract_inverted_index.robots. | 27 |
| abstract_inverted_index.usually | 53 |
| abstract_inverted_index.variant | 166 |
| abstract_inverted_index.without | 118 |
| abstract_inverted_index.workers | 20 |
| abstract_inverted_index.Learning | 0 |
| abstract_inverted_index.accurate | 162 |
| abstract_inverted_index.adequate | 116 |
| abstract_inverted_index.advance. | 141 |
| abstract_inverted_index.assuming | 126 |
| abstract_inverted_index.complete | 137 |
| abstract_inverted_index.generate | 112 |
| abstract_inverted_index.learning | 32, 151 |
| abstract_inverted_index.problems | 85 |
| abstract_inverted_index.proposed | 107, 143 |
| abstract_inverted_index.solution | 82 |
| abstract_inverted_index.training | 41, 138 |
| abstract_inverted_index.available | 29, 61 |
| abstract_inverted_index.existence | 38 |
| abstract_inverted_index.extension | 97 |
| abstract_inverted_index.factories | 22 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.piecewise | 89 |
| abstract_inverted_index.processed | 54 |
| abstract_inverted_index.streaming | 128, 173 |
| abstract_inverted_index.Evaluation | 102 |
| abstract_inverted_index.algorithm. | 101 |
| abstract_inverted_index.applicable | 146 |
| abstract_inverted_index.comparison | 123 |
| abstract_inverted_index.complexity | 70, 117 |
| abstract_inverted_index.envisioned | 15 |
| abstract_inverted_index.industrial | 157 |
| abstract_inverted_index.techniques | 30 |
| abstract_inverted_index.technology | 6 |
| abstract_inverted_index.experiments | 103 |
| abstract_inverted_index.incremental | 88, 127 |
| abstract_inverted_index.applications | 158 |
| abstract_inverted_index.availability | 134 |
| abstract_inverted_index.segmentation | 91 |
| abstract_inverted_index.side-by-side | 18 |
| abstract_inverted_index.Demonstration | 2 |
| abstract_inverted_index.demonstration | 34, 153 |
| abstract_inverted_index.pre-segmented | 50 |
| abstract_inverted_index.demonstrations | 44, 130 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.68860856 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |