add-tsvs

.gitattributes

@@ -52,5 +52,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-authors.tsv filter=lfs diff=lfs merge=lfs -text
-papers.tsv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -24,16 +24,8 @@ tags:
 task_categories:
 - other
 task_ids: []
-paperswithcode_id: d3
-dataset_info:
-- config_name: papers
-  download_size: 15876152
-  dataset_size: 15876152
-- config_name: authors
-  download_size: 1177888
-  dataset_size: 1177888
 ---
-# Dataset Card for DBLP Discovery Dataset (D3)
+# Dataset Card for [Dataset Name]
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
@@ -59,114 +51,52 @@ dataset_info:
   - [Citation Information](#citation-information)
   - [Contributions](#contributions)
 ## Dataset Description
-- **Repository:** https://github.com/jpwahle/lrec22-d3-dataset
-- **Paper:** https://aclanthology.org/2022.lrec-1.283/
-- **Total size:** 8.71 GB
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
 ### Dataset Summary
-DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers’ abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.
+[More Information Needed]
 ### Supported Tasks and Leaderboards
 [More Information Needed]
 ### Languages
-English
+[More Information Needed]
 ## Dataset Structure
 ### Data Instances
-Total size: 8.71 GB
-Papers size: 8.13 GB
-Authors size: 0.58 GB
+[More Information Needed]
 ### Data Fields
-#### Papers
-| Feature | Description |
-| --- | --- |
-| `corpusid` | The unique identifier of the paper. |
-| `externalids` | The same paper in other repositories (e.g., DOI, ACL). |
-| `title` | The title of the paper. |
-| `authors` | The authors of the paper with their `authorid` and `name`. |
-| `venue` | The venue of the paper. |
-| `year` | The year of the paper publication. |
-| `publicationdate` | A more precise publication date of the paper. |
-| `abstract` | The abstract of the paper. |
-| `outgoingcitations` | The number of references of the paper. |
-| `ingoingcitations` | The number of citations of the paper. |
-| `isopenaccess` | Whether the paper is open access. |
-| `influentialcitationcount` | The number of influential citations of the paper according to SemanticScholar. |
-| `s2fieldsofstudy` | The fields of study of the paper according to SemanticScholar. |
-| `publicationtypes` | The publication types of the paper. |
-| `journal` | The journal of the paper. |
-| `updated` | The last time the paper was updated. |
-| `url` | A url to the paper in SemanticScholar. |
-
-#### Authors
-| Feature | Description |
-| --- | --- |
-| `authorid` | The unique identifier of the author. |
-| `externalids` | The same author in other repositories (e.g., ACL, PubMed). This can include `ORCID` |
-| `name` | The name of the author. |
-| `affiliations` | The affiliations of the author. |
-| `homepage` | The homepage of the author. |
-| `papercount` | The number of papers the author has written. |
-| `citationcount` | The number of citations the author has received. |
-| `hindex` | The h-index of the author. |
-| `updated` | The last time the author was updated. |
-| `email` | The email of the author. |
-| `s2url` | A url to the author in SemanticScholar. |
+[More Information Needed]
 ### Data Splits
-- `papers`
-- `authors`
+[More Information Needed]
 ## Dataset Creation
 ### Curation Rationale
-Providing a resource to analyze the state of computer science research statistically and semantically.
+[More Information Needed]
 ### Source Data
 #### Initial Data Collection and Normalization
-DBLP and from v2.0 SemanticScholar
+[More Information Needed]
+#### Who are the source language producers?
+[More Information Needed]
+### Annotations
+#### Annotation process
+[More Information Needed]
+#### Who are the annotators?
+[More Information Needed]
+### Personal and Sensitive Information
+[More Information Needed]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
 ## Additional Information
 ### Dataset Curators
-[Jan Philip Wahle](https://jpwahle.com/)
+[More Information Needed]
 ### Licensing Information
-The DBLP Discovery Dataset is released under the CC BY-NC 4.0. By using this corpus, you are agreeing to its usage terms.
+[More Information Needed]
 ### Citation Information
-If you use the dataset in any way, please cite:
-```bib
-@inproceedings{Wahle2022c,
-  title        = {D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research},
-  author       = {Wahle, Jan Philip and Ruas, Terry and Mohammad, Saif M. and Gipp, Bela},
-  year         = {2022},
-  month        = {July},
-  booktitle    = {Proceedings of The 13th Language Resources and Evaluation Conference},
-  publisher    = {European Language Resources Association},
-  address      = {Marseille, France},
-  doi          = {},
-}
-```
-Also make sure to cite the following papers if you use SemanticScholar data:
-```bib
-@inproceedings{ammar-etal-2018-construction,
-    title = "Construction of the Literature Graph in Semantic Scholar",
-    author = "Ammar, Waleed  and
-      Groeneveld, Dirk  and
-      Bhagavatula, Chandra  and
-      Beltagy, Iz",
-    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
-    month = jun,
-    year = "2018",
-    address = "New Orleans - Louisiana",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/N18-3011",
-    doi = "10.18653/v1/N18-3011",
-    pages = "84--91",
-}
-```
-```bib
-@inproceedings{lo-wang-2020-s2orc,
-    title = "{S}2{ORC}: The Semantic Scholar Open Research Corpus",
-    author = "Lo, Kyle  and Wang, Lucy Lu  and Neumann, Mark  and Kinney, Rodney  and Weld, Daniel",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.447",
-    doi = "10.18653/v1/2020.acl-main.447",
-    pages = "4969--4983"
-}
-```### Contributions
-Thanks to [@jpwahle](https://github.com/jpwahle) for adding this dataset.
+[More Information Needed]
+### Contributions
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

dblp-discovery-dataset.py

@@ -1,189 +0,0 @@
-# coding=utf-8
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""D3: A Massive Dataset of Scholarly Metadata for Analyzing Computer Science Research"""
-
-import json
-import os
-from typing import Any, Dict, Generator, List, Optional, Tuple, Union
-
-import datasets
-from datasets.tasks import TextClassification
-from lxml import etree
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{wahle-etal-2022-d3,
-    title = "D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research",
-    author = "Wahle, Jan Philip  and
-      Ruas, Terry  and
-      Mohammad, Saif  and
-      Gipp, Bela",
-    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
-    month = jun,
-    year = "2022",
-    address = "Marseille, France",
-    publisher = "European Language Resources Association",
-    url = "https://aclanthology.org/2022.lrec-1.283",
-    pages = "2642--2651",
-    abstract = "DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (15{\%} annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers{'} abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.",
-}
-"""
-
-_DESCRIPTION = """This repository provides metadata to papers from DBLP."""
-
-_HOMEPAGE = "https://github.com/jpwahle/lrec22-d3-dataset"
-
-_LICENSE = (
-    "DBLP Discovery Dataset (D3) is licensed under a Creative Commons"
-    " Attribution-NonCommercial-ShareAlike 4.0 International License."
-)
-
-_URLS = [
-    "https://zenodo.org/record/7071698/files/2022-11-30-authors.jsonl.gz?download=1"
-    "https://zenodo.org/record/7071698/files/2022-11-30-papers.jsonl.gz?download=1",
-]
-
-
-class D3Config(datasets.BuilderConfig):
-    """BuilderConfig for GLUE."""
-
-    def __init__(
-        self,
-        features,
-        data_url,
-        data_dir,
-        **kwargs,
-    ):
-        super(D3Config, self).__init__(
-            version=datasets.Version("2.0.0", ""), **kwargs
-        )
-        self.features = features
-        self.data_url = data_url
-        self.data_dir = data_dir
-
-
-class D3(datasets.GeneratorBasedBuilder):
-    """D3 dataset."""
-
-    BUILDER_CONFIGS = [
-        D3Config(
-            name="papers",
-            features={
-                "corpusid": datasets.Value("int64"),
-                "title": datasets.Value("string"),
-                "authors": datasets.Sequence(
-                    {
-                        "authorId": datasets.Value("int64"),
-                        "name": datasets.Value("string"),
-                    }
-                ),
-                "venue": datasets.Value("string"),
-                "year": datasets.Value("int16"),
-                "publicationdate": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "referencecount": datasets.Value("int64"),
-                "citationcount": datasets.Value("int64"),
-                "isopenaccess": datasets.Value("bool"),
-                "influentialcitationcount": datasets.Value("int64"),
-                "s2fieldsofstudy": datasets.Sequence(
-                    {
-                        "category": datasets.Value("string"),
-                        "source": datasets.Value("string"),
-                    }
-                ),
-                "publicationtypes": datasets.Sequence(
-                    datasets.Value("string")
-                ),
-                "journal": datasets.Value("string"),
-                "updated": datasets.Value("string"),
-                "url": datasets.Value("string"),
-                "externalids": {
-                    "ACL": datasets.Value("string"),
-                    "DBLP": datasets.Value("string"),
-                    "ArXiv": datasets.Value("string"),
-                    "MAG": datasets.Value("string"),
-                    "CorpusId": datasets.Value("string"),
-                    "PubMed": datasets.Value("string"),
-                    "DOI": datasets.Value("string"),
-                    "PubMedCentral": datasets.Value("string"),
-                },
-                "syntactic": datasets.Sequence(datasets.Value("string")),
-                "semantic": datasets.Sequence(datasets.Value("string")),
-                "union": datasets.Sequence(datasets.Value("string")),
-                "enhanced": datasets.Sequence(datasets.Value("string"))
-            },
-            data_url="https://zenodo.org/record/7071698/files/2022-11-30-papers.jsonl.gz?download=1",
-            data_dir="papers",
-        ),
-        D3Config(
-            name="authors",
-            features={
-                "authorid": datasets.Value("int64"),
-                "name": datasets.Value("string"),
-                "homepage": datasets.Value("string"),
-                "papercount": datasets.Value("int64"),
-                "citationcount": datasets.Value("int64"),
-                "hindex": datasets.Value("int64"),
-                "aliases": datasets.Sequence(datasets.Value("string")),
-                "affiliations": datasets.Sequence(datasets.Value("string")),
-                "updated": datasets.Value("string"),
-                "s2url": datasets.Value("string"),
-                "externalids": {
-                    "DBLP": datasets.Value("string"),
-                    "ORCID": datasets.Value("string"),
-                }
-            },
-            data_url="https://zenodo.org/record/7071698/files/2022-11-30-authors.jsonl.gz?download=1",
-            data_dir="authors",
-        ),
-    ]
-
-    def _info(self):
-        features = datasets.Features(self.config.features)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_file = dl_manager.download_and_extract(self.config.data_url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": dl_manager.iter_files(data_file),
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths, split):
-        """Yields examples."""
-        for train_files in filepaths:
-            with open(train_files, encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    # For none open access papers, the abstract is not in the dataset
-                    # Replace it with an empty string
-                    if "abstract" not in data and self.config.name == "papers":
-                        data["abstract"] = ""
-                    yield id_, data