Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Large-scale Industrial and Professional Occupation Dataset   There has been growing interest in utilizing occupational data mining and
analysis. In today's job market, occupational data mining and analysis is
growing in importance as it enables companies to predict employee turnover,
model career trajectories, screen through resumes and perform other human
resource tasks. A key requirement to facilitate these tasks is the need for an
occupation-related dataset. However, most research use proprietary datasets or
do not make their dataset publicly available, thus impeding development in this
area. To solve this issue, we present the Industrial and Professional
Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k
LinkedIn users. In addition to making IPOD publicly available, we also: (i)
manually annotate each job title with its associated level of seniority, domain
of work and location; and (ii) provide embedding for job titles and discuss
various use cases. This dataset is publicly available at
https://github.com/junhua/ipod.

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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex