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  - materials science
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  - materials science
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  size_categories:
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  - 100K<n<1M
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+ ---
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+ # SciRIFF
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+
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+ The SciRIFF dataset includes 137K instruction-following demonstrations for 54 scientific literature understanding tasks. The tasks cover five essential scientific literature categories and span five domains.
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+
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+ ## License
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+
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+ SciRIFF is licensed under `ODC-By`.
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+
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+ ## Task provenance
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+
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+ SciRIFF was created by repurposing existing scientific literature understanding datasets. Below we provide information on the source data for each SciRIFF task, including license information on individual datasets where available.
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+
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+ | SciRIFF Name | Paper Link | License | Website / Download Link |
149
+ | :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------- | :----------------------------------------------------------------------------------------- |
150
+ | `acl_arc_intent_classification` | [ACL ARC](https://aclanthology.org/L08-1005/) | - | <https://github.com/allenai/scicite/> |
151
+ | `anat_em_ner` | [AnatEM](https://academic.oup.com/bioinformatics/article/30/6/868/285282) | CC BY | <https://nactem.ac.uk/anatomytagger/#AnatEM> |
152
+ | `annotated_materials_syntheses_events` | [Materials Science Procedural Text Corpus](https://aclanthology.org/W19-4007/) | MIT | <https://github.com/olivettigroup/annotated-materials-syntheses> |
153
+ | `bc7_litcovid_topic_classification` | [BioCreative VII LitCOVID](https://pubmed.ncbi.nlm.nih.gov/36043400/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/> |
154
+ | `bioasq_{factoid,general,list,yesno}_qa` | [BioASQ](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6) | CC BY | <http://bioasq.org/> |
155
+ | `biored_ner` | [BioRED](https://academic.oup.com/bib/article/23/5/bbac282/6645993) | - | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/> |
156
+ | `cdr_ner` | [BioCreative V CDR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/> |
157
+ | `chemdner_ner` | [CHEMDNER](https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-7-S1-S2) | - | <https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/> |
158
+ | `chemprot_{ner,re}` | [BioCreative VI ChemProt](https://www.semanticscholar.org/paper/Overview-of-the-BioCreative-VI-chemical-protein-Krallinger-Rabal/eed781f498b563df5a9e8a241c67d63dd1d92ad5) | - | <https://biocreative.bioinformatics.udel.edu/news/corpora/chemprot-corpus-biocreative-vi/> |
159
+ | `chemsum_single_document_summarization` | [ChemSum](https://aclanthology.org/2023.acl-long.587/) | - | <https://github.com/griff4692/calibrating-summaries> |
160
+ | `chemtables_te` | [ChemTables](https://arxiv.org/abs/2305.14336) | GPL 3.0 | <https://huggingface.co/datasets/fbaigt/schema-to-json> |
161
+ | `chia_ner` | [Chia](https://www.nature.com/articles/s41597-020-00620-0) | CC BY | <https://github.com/WengLab-InformaticsResearch/CHIA> |
162
+ | `covid_deepset_qa` | [COVID-QA](https://aclanthology.org/2020.nlpcovid19-acl.18/) | Apache 2.0 | <https://github.com/deepset-ai/COVID-QA> |
163
+ | `covidfact_entailment` | [CovidFact](https://aclanthology.org/2021.acl-long.165/) | - | <https://github.com/asaakyan/covidfact> |
164
+ | `craftchem_ner` | [CRAFT-Chem](https://link.springer.com/chapter/10.1007/978-94-024-0881-2_53) | - | <https://huggingface.co/datasets/ghadeermobasher/CRAFT-Chem> |
165
+ | `data_reco_mcq_{mc,sc}` | [DataFinder](https://aclanthology.org/2023.acl-long.573/) | Apache 2.0 | <https://github.com/viswavi/datafinder/tree/main> |
166
+ | `ddi_ner` | [DDI](https://www.sciencedirect.com/science/article/pii/S1532046413001123) | CC BY | <https://github.com/isegura/DDICorpus> |
167
+ | `discomat_te` | [DISCoMaT](https://aclanthology.org/2023.acl-long.753/) | CC BY-SA | <https://github.com/M3RG-IITD/DiSCoMaT> |
168
+ | `drug_combo_extraction_re` | [Drug Combinations](https://aclanthology.org/2022.naacl-main.233/) | - | <https://github.com/allenai/drug-combo-extraction> |
169
+ | `evidence_inference` | [Evidence inference](https://aclanthology.org/2020.bionlp-1.13/) | MIT | <https://evidence-inference.ebm-nlp.com/> |
170
+ | `genia_ner` | [JNLPBA](https://aclanthology.org/W04-1213/) | CC BY | <https://github.com/spyysalo/jnlpba> |
171
+ | `gnormplus_ner` | [GNormPlus](https://www.hindawi.com/journals/bmri/2015/918710/) | - | <https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/> |
172
+ | `healthver_entailment` | [HealthVer](https://aclanthology.org/2021.findings-emnlp.297/) | nan | <https://github.com/sarrouti/healthver> |
173
+ | `linnaeus_ner` | [LINNAEUS](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) | CC BY | <https://sourceforge.net/projects/linnaeus/> |
174
+ | `medmentions_ner` | [MedMentions](https://arxiv.org/abs/1902.09476) | CC 0 | <https://github.com/chanzuckerberg/MedMentions> |
175
+ | `mltables_te` | [AxCell](https://aclanthology.org/2020.emnlp-main.692/) | Apache 2.0 | <https://github.com/paperswithcode/axcell> |
176
+ | `mslr2022_cochrane_multidoc_summarization` | [Cochrane](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378607/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
177
+ | `mslr2022_ms2_multidoc_summarization` | [MS^2](https://aclanthology.org/2021.emnlp-main.594/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
178
+ | `multicite_intent_classification` | [MultiCite](https://aclanthology.org/2022.naacl-main.137/) | CC BY-NC | <https://github.com/allenai/multicite> |
179
+ | `multixscience_multidoc_summarization` | [Multi-XScience](https://aclanthology.org/2020.emnlp-main.648/) | MIT | <https://github.com/yaolu/Multi-XScience> |
180
+ | `mup_single_document_summarization` | [MUP](https://aclanthology.org/2022.sdp-1.32/) | Apache 2.0 | <https://github.com/allenai/mup> |
181
+ | `ncbi_ner` | [NCBI Disease](https://pubmed.ncbi.nlm.nih.gov/24393765/) | CC 0 | <https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/> |
182
+ | `nlmchem_ner` | [NLM-Chem](https://pubmed.ncbi.nlm.nih.gov/33767203/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/> |
183
+ | `nlmgene_ner` | [NLM-Gene](https://pubmed.ncbi.nlm.nih.gov/33839304/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/> |
184
+ | `pico_ner` | [EBM-NLP PICO](https://aclanthology.org/P18-1019/) | - | <https://github.com/bepnye/EBM-NLP> |
185
+ | `pubmedqa_qa` | [PubMedQA](https://aclanthology.org/D19-1259/) | MIT | <https://github.com/pubmedqa/pubmedqa> |
186
+ | `qasa_abstractive_qa` | [QASA](https://proceedings.mlr.press/v202/lee23n) | MIT | <https://github.com/lgresearch/QASA> |
187
+ | `qasper_{abstractive,extractive}_qa` | [Qasper](https://aclanthology.org/2021.naacl-main.365/) | CC BY | <https://allenai.org/data/qasper> |
188
+ | `scicite_classification` | [SciCite](https://aclanthology.org/N19-1361/) | - | <https://allenai.org/data/scicite> |
189
+ | `scientific_lay_summarisation_`<br>`{elife,plos}_single_doc_summ` | [Lay Summarisation](https://aclanthology.org/2022.emnlp-main.724/) | - | <https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation> |
190
+ | `scientific_papers_summarization_`<br>`single_doc_{arxiv,pubmed}` | [Scientific Papers](https://aclanthology.org/N18-2097/) | - | <https://huggingface.co/datasets/armanc/scientific_papers> |
191
+ | `scierc_{ner,re}` | [SciERC](https://aclanthology.org/D18-1360/) | - | <http://nlp.cs.washington.edu/sciIE/> |
192
+ | `scifact_entailment` | [SciFact](https://aclanthology.org/2020.emnlp-main.609/) | CC BY-NC | <https://allenai.org/data/scifact> |
193
+ | `scireviewgen_multidoc_summarization` | [SciReviewGen](https://aclanthology.org/2023.findings-acl.418/) | CC BY-NC | <https://github.com/tetsu9923/SciReviewGen> |
194
+ | `scitldr_aic` | [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428/) | Apache 2.0 | <https://github.com/allenai/scitldr> |
card.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # SciRIFF
2
+
3
+ The SciRIFF dataset includes 137K instruction-following demonstrations for 54 scientific literature understanding tasks. The tasks cover five essential scientific literature categories and span five domains.
4
+
5
+ ## License
6
+
7
+ SciRIFF is licensed under `ODC-By`.
8
+
9
+ ## Task provenance
10
+
11
+ SciRIFF was created by repurposing existing scientific literature understanding datasets. Below we provide information on the source data for each SciRIFF task, including license information on individual datasets where available.
12
+
13
+ | SciRIFF Name | Paper Link | License | Website / Download Link |
14
+ | :---------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------- | :----------------------------------------------------------------------------------------- |
15
+ | `acl_arc_intent_classification` | [ACL ARC](https://aclanthology.org/L08-1005/) | - | <https://github.com/allenai/scicite/> |
16
+ | `anat_em_ner` | [AnatEM](https://academic.oup.com/bioinformatics/article/30/6/868/285282) | CC BY | <https://nactem.ac.uk/anatomytagger/#AnatEM> |
17
+ | `annotated_materials_syntheses_events` | [Materials Science Procedural Text Corpus](https://aclanthology.org/W19-4007/) | MIT | <https://github.com/olivettigroup/annotated-materials-syntheses> |
18
+ | `bc7_litcovid_topic_classification` | [BioCreative VII LitCOVID](https://pubmed.ncbi.nlm.nih.gov/36043400/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/> |
19
+ | `bioasq_{factoid,general,list,yesno}_qa` | [BioASQ](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0564-6) | CC BY | <http://bioasq.org/> |
20
+ | `biored_ner` | [BioRED](https://academic.oup.com/bib/article/23/5/bbac282/6645993) | - | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/> |
21
+ | `cdr_ner` | [BioCreative V CDR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/) | - | <https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/> |
22
+ | `chemdner_ner` | [CHEMDNER](https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-7-S1-S2) | - | <https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/> |
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+ | `chemprot_{ner,re}` | [BioCreative VI ChemProt](https://www.semanticscholar.org/paper/Overview-of-the-BioCreative-VI-chemical-protein-Krallinger-Rabal/eed781f498b563df5a9e8a241c67d63dd1d92ad5) | - | <https://biocreative.bioinformatics.udel.edu/news/corpora/chemprot-corpus-biocreative-vi/> |
24
+ | `chemsum_single_document_summarization` | [ChemSum](https://aclanthology.org/2023.acl-long.587/) | - | <https://github.com/griff4692/calibrating-summaries> |
25
+ | `chemtables_te` | [ChemTables](https://arxiv.org/abs/2305.14336) | GPL 3.0 | <https://huggingface.co/datasets/fbaigt/schema-to-json> |
26
+ | `chia_ner` | [Chia](https://www.nature.com/articles/s41597-020-00620-0) | CC BY | <https://github.com/WengLab-InformaticsResearch/CHIA> |
27
+ | `covid_deepset_qa` | [COVID-QA](https://aclanthology.org/2020.nlpcovid19-acl.18/) | Apache 2.0 | <https://github.com/deepset-ai/COVID-QA> |
28
+ | `covidfact_entailment` | [CovidFact](https://aclanthology.org/2021.acl-long.165/) | - | <https://github.com/asaakyan/covidfact> |
29
+ | `craftchem_ner` | [CRAFT-Chem](https://link.springer.com/chapter/10.1007/978-94-024-0881-2_53) | - | <https://huggingface.co/datasets/ghadeermobasher/CRAFT-Chem> |
30
+ | `data_reco_mcq_{mc,sc}` | [DataFinder](https://aclanthology.org/2023.acl-long.573/) | Apache 2.0 | <https://github.com/viswavi/datafinder/tree/main> |
31
+ | `ddi_ner` | [DDI](https://www.sciencedirect.com/science/article/pii/S1532046413001123) | CC BY | <https://github.com/isegura/DDICorpus> |
32
+ | `discomat_te` | [DISCoMaT](https://aclanthology.org/2023.acl-long.753/) | CC BY-SA | <https://github.com/M3RG-IITD/DiSCoMaT> |
33
+ | `drug_combo_extraction_re` | [Drug Combinations](https://aclanthology.org/2022.naacl-main.233/) | - | <https://github.com/allenai/drug-combo-extraction> |
34
+ | `evidence_inference` | [Evidence inference](https://aclanthology.org/2020.bionlp-1.13/) | MIT | <https://evidence-inference.ebm-nlp.com/> |
35
+ | `genia_ner` | [JNLPBA](https://aclanthology.org/W04-1213/) | CC BY | <https://github.com/spyysalo/jnlpba> |
36
+ | `gnormplus_ner` | [GNormPlus](https://www.hindawi.com/journals/bmri/2015/918710/) | - | <https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/> |
37
+ | `healthver_entailment` | [HealthVer](https://aclanthology.org/2021.findings-emnlp.297/) | nan | <https://github.com/sarrouti/healthver> |
38
+ | `linnaeus_ner` | [LINNAEUS](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85) | CC BY | <https://sourceforge.net/projects/linnaeus/> |
39
+ | `medmentions_ner` | [MedMentions](https://arxiv.org/abs/1902.09476) | CC 0 | <https://github.com/chanzuckerberg/MedMentions> |
40
+ | `mltables_te` | [AxCell](https://aclanthology.org/2020.emnlp-main.692/) | Apache 2.0 | <https://github.com/paperswithcode/axcell> |
41
+ | `mslr2022_cochrane_multidoc_summarization` | [Cochrane](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378607/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
42
+ | `mslr2022_ms2_multidoc_summarization` | [MS^2](https://aclanthology.org/2021.emnlp-main.594/) | Apache 2.0 | <https://github.com/allenai/mslr-shared-task> |
43
+ | `multicite_intent_classification` | [MultiCite](https://aclanthology.org/2022.naacl-main.137/) | CC BY-NC | <https://github.com/allenai/multicite> |
44
+ | `multixscience_multidoc_summarization` | [Multi-XScience](https://aclanthology.org/2020.emnlp-main.648/) | MIT | <https://github.com/yaolu/Multi-XScience> |
45
+ | `mup_single_document_summarization` | [MUP](https://aclanthology.org/2022.sdp-1.32/) | Apache 2.0 | <https://github.com/allenai/mup> |
46
+ | `ncbi_ner` | [NCBI Disease](https://pubmed.ncbi.nlm.nih.gov/24393765/) | CC 0 | <https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/> |
47
+ | `nlmchem_ner` | [NLM-Chem](https://pubmed.ncbi.nlm.nih.gov/33767203/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/> |
48
+ | `nlmgene_ner` | [NLM-Gene](https://pubmed.ncbi.nlm.nih.gov/33839304/) | CC 0 | <https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene/> |
49
+ | `pico_ner` | [EBM-NLP PICO](https://aclanthology.org/P18-1019/) | - | <https://github.com/bepnye/EBM-NLP> |
50
+ | `pubmedqa_qa` | [PubMedQA](https://aclanthology.org/D19-1259/) | MIT | <https://github.com/pubmedqa/pubmedqa> |
51
+ | `qasa_abstractive_qa` | [QASA](https://proceedings.mlr.press/v202/lee23n) | MIT | <https://github.com/lgresearch/QASA> |
52
+ | `qasper_{abstractive,extractive}_qa` | [Qasper](https://aclanthology.org/2021.naacl-main.365/) | CC BY | <https://allenai.org/data/qasper> |
53
+ | `scicite_classification` | [SciCite](https://aclanthology.org/N19-1361/) | - | <https://allenai.org/data/scicite> |
54
+ | `scientific_lay_summarisation_`<br>`{elife,plos}_single_doc_summ` | [Lay Summarisation](https://aclanthology.org/2022.emnlp-main.724/) | - | <https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation> |
55
+ | `scientific_papers_summarization_`<br>`single_doc_{arxiv,pubmed}` | [Scientific Papers](https://aclanthology.org/N18-2097/) | - | <https://huggingface.co/datasets/armanc/scientific_papers> |
56
+ | `scierc_{ner,re}` | [SciERC](https://aclanthology.org/D18-1360/) | - | <http://nlp.cs.washington.edu/sciIE/> |
57
+ | `scifact_entailment` | [SciFact](https://aclanthology.org/2020.emnlp-main.609/) | CC BY-NC | <https://allenai.org/data/scifact> |
58
+ | `scireviewgen_multidoc_summarization` | [SciReviewGen](https://aclanthology.org/2023.findings-acl.418/) | CC BY-NC | <https://github.com/tetsu9923/SciReviewGen> |
59
+ | `scitldr_aic` | [SciTLDR](https://aclanthology.org/2020.findings-emnlp.428/) | Apache 2.0 | <https://github.com/allenai/scitldr> |