Points of Diversion (POD): Depicts the location of each water right diversion point (POD) and provides basic information about the associated water right. All current and individually held water rights are shown in this data set except for those held by irrigation districts, applications, temporary transfers, instream leases, and limited licenses.Current code definitions at: https://www.oregon.gov/owrd/WRDFormsPDF/wris_code_key.pdf.Compilation procedures document at: https://arcgis.wrd.state.or.us/data/OWRD_WR_GIS_procedures.pdf. ----- Places of Use (POU): Depicts the location of each water right place of use (POU) polygon and provides basic information about the associated water right. All current and individually held water rights are shown in this data set except for those held by irrigation districts, applications, temporary transfers, instream leases, and limited licenses.Current code definitions at: https://www.oregon.gov/owrd/WRDFormsPDF/wris_code_key.pdf.Compilation procedures document at: https://arcgis.wrd.state.or.us/data/OWRD_WR_GIS_procedures.pdf.

This dataset contains around 1300 positive and negative Bengal ( Bangla ) sentiment words. This lexicon was created from a Bengali review corpus.

If you use this lexicon please cite following paper-

@inproceedings{sazzed2020development, title={Development of Sentiment Lexicon in Bengali utilizing Corpus and Cross-lingual Resources}, author={Sazzed, Salim}, booktitle={2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)}, pages={237--244}, year={2020}, organization={IEEE Computer Society} }

https://www.cs.odu.edu/~ssazzed/IEEE_IRI_2020.pdf

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Location of all current mineral exploration licences issued under the Mining Act 1971. An exploration licence (EL) authorises the licensee, subject to the Act, Regulations and conditions of the licence, to explore for all minerals and/or opal other than extractive minerals (i.e. building construction materials). Licences are normally granted for a 1 or 2 year term. Application for renewal(s) up to maximum of 5 years and a subsequent licence can be made online (SARIG) or via a manual forms available on the DMITRE Minerals web site http://www.pir.sa.gov.au/minerals/forms_and_guidelines/prescribed_forms. An EL excludes current production mining tenements and any other areas not available for exploration. The M05 information sheet provides extended information on exploration licence general conditions and procedures https://sarigbasis.pir.sa.gov.au/WebtopEw/ws/samref/sarig1/image/DDD/ISM05.pdf

Data is available for download from SARIG in formats described in access section below (see document SAExplorationLicencesCurrent_MineralOpal_SHP.htm stored with the dataset).

Purpose

The dataset was developed to record information necessary for the administration of the Mining Act.

Use: Used to supply government, industry and the general public with an up-to-date status and extent of mineral exploration activities throughout the state.

Dataset History

Source data history: Exploration licence boundaries were sourced from the official Mining Register licence application documents. Licence boundaries are legally defined to follow lines of latitude and longitude. The register has existed since 1930. Processing steps: Coordinates are entered from licence documents. Linework is cleaned to remove duplicate arcs. Data is adjusted for accurate state borders, coastline, areas reserved from the Mining Act and areas of no access for mineral exploration or production. Legal description is defined in AGD66 whilst data is spatially managed in GDA94 to conform to other spatial data. The data is updated in SARIG daily.

Dataset Citation

"SA Department for Manufacturing, Innovation, Trade, Resources and Energy" (2014) SA Mineral and/or Opal Exploration Licenses. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/f54faf14-213f-4eb0-ad65-eecb0d68a8e2.

1.Framework overview. This paper proposed a pipeline to construct high-quality datasets for text mining in materials science. Firstly, we utilize the traceable automatic acquisition scheme of literature to ensure the traceability of textual data. Then, a data processing method driven by downstream tasks is performed to generate high-quality pre-annotated corpora conditioned on the characteristics of materials texts. On this basis, we define a general annotation scheme derived from materials science tetrahedron to complete high-quality annotation. Finally, a conditional data augmentation model incorporating materials domain knowledge (cDA-DK) is constructed to augment the data quantity.2.Dataset information. The experimental datasets used in this paper include: the Matscholar dataset publicly published by Weston et al. (DOI: 10.1021/acs.jcim.9b00470), and the NASICON entity recognition dataset constructed by ourselves. Herein, we mainly introduce the details of NASICON entity recognition dataset.2.1 Data collection and preprocessing. Firstly, 55 materials science literature related to NASICON system are collected through Crystallographic Information File (CIF), which contains a wealth of structure-activity relationship information. Note that materials science literature is mostly stored as portable document format (PDF), with content arranged in columns and mixed with tables, images, and formulas, which significantly compromises the readability of the text sequence. To tackle this issue, we employ the text parser PDFMiner (a Python toolkit) to standardize, segment, and parse the original documents, thereby converting PDF literature into plain text. In this process, the entire textual information of literature, encompassing title, author, abstract, keywords, institution, publisher, and publication year, is retained and stored as a unified TXT document. Subsequently, we apply rules based on Python regular expressions to remove redundant information, such as garbled characters and line breaks caused by figures, tables, and formulas. This results in a cleaner text corpus, enhancing its readability and enabling more efficient data analysis. Note that special symbols may also appear as garbled characters, but we refrain from directly deleting them, as they may contain valuable information such as chemical units. Therefore, we converted all such symbols to a special token

This map was created by the Department of Primary Industries (Office of Water) in 2013 to identify areas in NSW with a reliable surface water supply. Land with reliable surface water was defined as areas:

buffering all regulated rivers and creeks by 150 metres,
buffering unregulated rivers and creeks with a 5th stream order or higher, by 150 metres,
buffering 3rd and 4th stream order unregulated rivers and creeks, by 150 metres

Reliable surface water mapping along with two other datasets (rainfall of 350mm for more per annum - 9 out of 10 years and highly productive groundwater) are used to identify land with access to a reliable water supply, forming part of the regional and site level assessment classification of Biophysical Strategic Agricultural Land (BSAL). Under the Mining SEPP, all State Significant Development applications require a Site Verification Certificate to determine if their site contains any BSAL and therefore requiring further assessment from the Mining and Petroleum Gateway Panel. This process is managed by Planning and Assessment, Department of Planning, Industry and Environment and are custodian of the dataset. A pdf map and GIS shapefile of this dataset is accessible from the resources section of the metadata.