cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/6xr2rp8gvh.3
title: Computationally grounded semantic similarity datasets for Basque and Spanish

description: The current word similarity datasets address a gap in psycholinguistic research by providing a comprehensive
set of noun pairs with quantifications of semantic similarity. The datasets are based on two well-known Natural Language Processing
resources: text corpora and knowledge bases. It aims to facilitate research in lexical processing by incorporating variables that play
a significant role in semantic analysis.

The dataset included in this repository provides noun pairs' information in Basque and European Spanish. It offers a rich collection
of noun pairs with associated psycholinguistic features and word similarity measurements. Researchers can leverage this dataset to
explore semantic similarity and lexical processing across languages. 

In the dataset, each noun is associated with four linguistic features: concreteness (CNC), word frequency (FRQ), semantic neighborhood density (SND), and phonemic neighborhood density (PND). These features include their corresponding high- and low-valued clusters. The matching of noun pairs is entirely computational and follows these steps: for each noun, we search for every noun that matches the clusters across all four features. For each matching noun pair, we compute three types of word similarity: text embeddings similarity (SIM_TXT), WordNet-based embeddings similarity (SIM_WN) and hybrid embeddings similarity (SIM_HYB)

The dataset includes the following columns in each line:

 - noun1
 - noun2
 - SIM_TXT
 - SIM_WN 
 - SIM_HYB 
 - CNC value of noun1.
 - Cluster identifier for the CNC of noun1.
 - CNC value of noun2.
 - Cluster identifier for the CNC of noun2.
 - FRQ of noun1.
 - Cluster identifier for the FRQ of noun1.
 - FRQ of noun2.
 - Cluster identifier for the FRQ of noun2.
 - PND of noun1.
 - Cluster identifier for the PND of noun1.
 - PND of noun2.
 - Cluster identifier for the PND of noun2.
 - SND of noun1.
 - Cluster identifier for the SND of noun1.
 - SND of noun2.
 - Cluster identifier for the SND of noun2.

The feature dictionaries utilized for creating the word similarity datasets are included in the repository. Each line of the dictionary is comprised by the noun, its corresponding feature cluster, the normalized value of the feature measurement, the raw value of the feature measurement.
DOI: 10.17632/6xr2rp8gvh.4
title: Computationally grounded semantic similarity datasets for Basque and Spanish

description: The current word similarity datasets address a gap in psycholinguistic research by providing a comprehensive
set of noun pairs with quantifications of semantic similarity. The datasets are based on two well-known Natural Language Processing
resources: text corpora and knowledge bases. It aims to facilitate research in lexical processing by incorporating variables that play
a significant role in semantic analysis.

The dataset included in this repository provides noun pairs' information in Basque and European Spanish. It offers a rich collection
of noun pairs with associated psycholinguistic features and word similarity measurements. Researchers can leverage this dataset to
explore semantic similarity and lexical processing across languages. 

In the dataset, each noun is associated with four linguistic features: concreteness (CNC), word frequency (FRQ), semantic neighborhood density (SND), and phonemic neighborhood density (PND). These features include their corresponding high- and low-valued clusters. The matching of noun pairs is entirely computational and follows these steps: for each noun, we search for every noun that matches the clusters across all four features. For each matching noun pair, we compute three types of word similarity: text embeddings similarity (SIM_TXT), WordNet-based embeddings similarity (SIM_WN) and hybrid embeddings similarity (SIM_HYB)

The dataset includes the following columns in each line:

 - noun1
 - noun2
 - SIM_TXT
 - SIM_WN 
 - SIM_HYB 
 - CNC value of noun1.
 - Cluster identifier for the CNC of noun1.
 - CNC value of noun2.
 - Cluster identifier for the CNC of noun2.
 - FRQ of noun1.
 - Cluster identifier for the FRQ of noun1.
 - FRQ of noun2.
 - Cluster identifier for the FRQ of noun2.
 - PND of noun1.
 - Cluster identifier for the PND of noun1.
 - PND of noun2.
 - Cluster identifier for the PND of noun2.
 - SND of noun1.
 - Cluster identifier for the SND of noun1.
 - SND of noun2.
 - Cluster identifier for the SND of noun2.

The feature dictionaries utilized for creating the word similarity datasets are included in the repository. Each line of the dictionary is comprised by the noun, its corresponding feature cluster, the normalized value of the feature measurement, the raw value of the feature measurement.
DOI: 10.15468/dl.xpnywf
title: Occurrence Download

description: A dataset containing 3360 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carya tomentosa (Lam.) Nutt."
 ]
}

 The dataset includes 3360 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013372-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ppfvc65m2v.1
title: A Python Code for Statistical Mirroring

description: Definition 
Statistical mirroring is the measure of the proximity or deviation of transformed data points from a specified location estimate within a given distribution. Within the framework of Kabirian-based optinalysis, statistical mirroring is conceptualized as the isoreflectivity of the transformed data points to a defined statistical mirror. This statistical mirror is an amplified location estimate of the distribution, achieved through a specified size or length. The location estimate may include parameters such as the mean, median, mode, maximum, minimum, or a reference value.

The process of statistical mirroring comprises two distinct phases: 
a) Preprocessing phase: This involves applying preprocessing transformations, such as compulsory theoretical ordering, with or without centering the data. It also encompasses tasks like statistical mirror design and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the preprocessed data and its designed statistical mirror. 
b) Optinalytic model calculation phase: This phase is focused on computing estimates based on Kabirian-based isomorphic optinalysis models.
DOI: 10.17632/ppfvc65m2v.2
title: A Python Code for Statistical Mirroring

description: Definition 
Statistical mirroring is the measure of the proximity or deviation of transformed data points from a specified location estimate within a given distribution. Within the framework of Kabirian-based optinalysis, statistical mirroring is conceptualized as the isoreflectivity of the transformed data points to a defined statistical mirror. This statistical mirror is an amplified location estimate of the distribution, achieved through a specified size or length. The location estimate may include parameters such as the mean, median, mode, maximum, minimum, or reference value.

The process of statistical mirroring comprises two distinct phases: 
a) Preprocessing phase: This involves applying preprocessing transformations, such as compulsory theoretical ordering, with or without centering the data. It also encompasses tasks like statistical mirror design and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the preprocessed data and its designed statistical mirror. 
b) Optinalytic model calculation phase: This phase is focused on computing estimates based on Kabirian-based isomorphic optinalysis models.
DOI: 10.17632/ppfvc65m2v.3
title: A Python Code for Statistical Mirroring

description: Definition 
Statistical mirroring is the measure of the proximity or deviation of transformed data points from a specified location estimate within a given distribution. Within the framework of Kabirian-based optinalysis, statistical mirroring is conceptualized as the isoreflectivity of the transformed data points to a defined statistical mirror. This statistical mirror is an amplified location estimate of the distribution, achieved through a specified size or length. The location estimate may include parameters such as the mean, median, mode, maximum, minimum, or reference value.

The process of statistical mirroring comprises two distinct phases: 
a) Preprocessing phase: This involves applying preprocessing transformations, such as compulsory theoretical ordering, with or without centering the data. It also encompasses tasks like statistical mirror design and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the preprocessed data and its designed statistical mirror. 
b) Optinalytic model calculation phase: This phase is focused on computing estimates based on Kabirian-based isomorphic optinalysis models.
DOI: 10.17632/77b9k3s5kb.1
title: Development of 2D Interactive Video in Medical Education: Enhancing Student Engagement and Learning in Biochemistry

description: This dataset contains information from a quasi-experimental study conducted to assess the effectiveness of a 2D interactive video designed to enhance student engagement, problem-solving abilities, and learning outcomes in biochemistry among first-year MBBS students. The video, focused on high anion gap metabolic acidosis (specifically diabetic ketoacidosis), was created using Animaker and included decision points for interactive learning. The study involved 148 students from the 2023-2024 MBBS batch, and feedback was collected through a post-intervention questionnaire. Data points include student perceptions of video clarity, effectiveness in teaching medical concepts, engagement, pacing, and the impact of interactive elements, as well as overall satisfaction with the learning experience.
DOI: 10.34740/kaggle/dsv/9620722
title: AutoXGB
DOI: 10.34740/kaggle/dsv/9620723
title: OnlineFree
DOI: 10.15468/dl.a8ffav
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "TaxonKey is Vespertilionidae"
 ]
}

 The dataset includes 103 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013373-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5518/445
title: Napa Valley Earthquake geodetic slip inversion tools and data

description: This dataset contains all the data required to run perform a geodetic slip inversion using the software slipBERI (https://doi.org/10.5518/444) for the 2014 Mw 6.0 Napa Valley, California, earthquake. Additionally this dataset contains the models runs of the solutions found in the associated thesis and paper.
DOI: 10.17632/gnrcj8s7fp.1
title: Python Codes for Kabirian-based Automorphic and Isomorphic Optinalysis 

description: Optinalysis is a function that autoreflectively or isoreflectively compares the symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within one or between two mathematical structures as a mirror-like (optic-like) reflection of each other about a central point.

Automorphic or shape optinalysis refers to the analysis of autoreflective pair of a mathematical structure by optinalysis. It is a method of symmetry/asymmetry and identity estimation.

Isomorphic or comparative optinalysis refers to the analysis between isoreflective pair of mathematical structures by optinalysis. It is a method of similarity/dissimilarity and identity estimation.
DOI: 10.17632/gnrcj8s7fp.2
title: Python Codes for Kabirian-based Automorphic and Isomorphic Optinalysis 

description: Optinalysis is a function that autoreflectively or isoreflectively compares the symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within one or between two mathematical structures as a mirror-like (optic-like) reflection of each other about a central point.

Automorphic or shape optinalysis analyzes autoreflective pairs of a mathematical structure by optinalysis. It is a method of symmetry/asymmetry and identity estimation.

Isomorphic or comparative optinalysis refers to the analysis of isoreflective pairs of mathematical structures by optinalysis. It is a method of similarity/dissimilarity and identity estimation.
DOI: 10.17632/gnrcj8s7fp.3
title: Python Codes for Kabirian-based Automorphic and Isomorphic Optinalysis

description: Optinalysis is a function that autoreflectively or isoreflectively compares the symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within one or between two mathematical structures as a mirror-like (optic-like) reflection of each other about a central point [1]. 
Automorphic or shape optinalysis analyzes autoreflective pairs of a mathematical structure by optinalysis. It is a method of symmetry/asymmetry and identity estimation [1]. 
Isomorphic or comparative optinalysis refers to the analysis of isoreflective pairs of mathematical structures by optinalysis. It is a method of similarity/dissimilarity and identity estimation [1].

Reference:
[1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400
DOI: 10.17632/nzv87z2y54.1
title: data for 'Oxygen, sulfur, calcium and carbonate clumped isotopes in sulfate-bearing calcite and aragonite' by Yigal Barkan, Chirantan Pramanik, Ruth Yam, Aldo Shemesh, Sierra V. Petersen, Arbia Jouini, Guillaume Caro, Guillaume Paris and Itay Halevy

description: Oxygen, sulfur, calcium and carbonate clumped isotope experimental datasets for calcite and aragonite samples analyzed in different laboratories
DOI: 10.17632/tnwpt54jnb.1
title: Python Code for Geometrical Pairwise Analysis of Biological Sequences Following Kabirian-based Isomorphic Optinalysis

description: Geometrical pairwise analysis (GPA) of biological sequences following Kabirian-based optinalysis looks at two biological sequences (i.e., DNA, RNA, and amino acid sequences) as isoreflective as a mirror-like reflection of each other about a central point. It estimates the percentage similarity between two sequences. 

In the context of biological sequence analysis, geometrical pairwise analysis refers to the empirical ordering, sequence alignment, numerical encoding, and finally optinalysing of the established isoreflective pair for the given sequences.
DOI: 10.17632/tnwpt54jnb.2
title: Python Code for Geometrical Pairwise Analysis of Biological Sequences Following Kabirian-based Isomorphic Optinalysis

description: Geometrical pairwise analysis (GPA) of biological sequences following Kabirian-based optinalysis looks at two biological sequences (i.e., DNA, RNA, and amino acid sequences) as isoreflective as a mirror-like reflection of each other about a central point. It estimates the percentage similarity between two sequences. 

In the context of biological sequence analysis, geometrical pairwise analysis refers to the empirical ordering, sequence alignment, numerical encoding, and finally optinalysing of the established isoreflective pair for the given sequences.
DOI: 10.17632/tnwpt54jnb.3
title: Python Code for Geometrical Pairwise Analysis of Biological Sequences Following Kabirian-based Isomorphic Optinalysis

description: Geometrical pairwise analysis (GPA) of biological sequences following Kabirian-based optinalysis looks at two biological sequences (i.e., DNA, RNA, and amino acid sequences) as isoreflective as a mirror-like reflection of each other about a central point [1]. It estimates the percentage similarity between two sequences. In the context of biological sequence analysis, geometrical pairwise analysis refers to the empirical ordering, sequence alignment, numerical encoding, and finally optinalysing of the established isoreflective pair for the given sequences [1].

Reference:
[1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400
DOI: 10.15468/dl.5v5wad
title: Occurrence Download

description: A dataset containing 478 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Abies alba Mill.",
 "Year 2000-2024"
 ]
}

 The dataset includes 478 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013375-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3tp2xf
title: Occurrence Download

description: A dataset containing 243860 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ESP.6.1_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2021-2021"
 ]
}

 The dataset includes 243860 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013346-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kt758yr9yy.1
title: Investigation of Transition to Environmental Remote Sensing and the Factors Influencing Effective Decisions on Soil Preparation and Sowing Timing: A Case Study

description: Data set gained throughout the experiment study including Informed consent form
DOI: 10.17632/5xf2ykkmr3.1
title: Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Formicidae: Formicinae) and it’s dynamic in oil palm plantations industry

description: An estimation of the Asian weaver ant population size (total workers: major and minor) by nest count method was performed in oil palm plantations. The hypothesis is that the total number of nests per colony multiplied by the average total workers in nests can provide an estimate of its population in a specific occupied area. These dataset analysis results shows that there is no relation between the nest distribution contents of individuals (major workers, minor workers, winged queens representing the population size-density) and the determination of nests characteristic parameters (sizes, height to the ground, volume). The findings hence confirming the hypothesis since the volume never had any influence as per requirements recommendations by Pinkalski et al (2015). 25.	Pinkalski, C.; Damgaard, C.; Jensen, K.V.; Gislum, R.; Peng, R.; Offenberg, J. Non-destructive biomass estimation of Oecophylla smaragdina colonies: a model species for the ecological impact of ants. Insect Conservation and Diversity, 2015, 8(5), 464–473. https://doi.org/10.1111/icad.12126.
DOI: 10.17632/5xf2ykkmr3.2
title: Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Formicidae: Formicinae) and it’s dynamic in oil palm plantations industry

description: 1.  An estimation of the Asian weaver ant population size (total workers: major and minor) by nest count method was performed in oil palm plantations. The hypothesis is that the total number of nests per colony multiplied by the average total workers in nests can provide an estimate of its population in a specific occupied area. These dataset analysis results shows that there is no relation between the nest distribution contents of individuals (major workers, minor workers, winged queens representing the population size-density) and the determination of nests characteristic parameters (sizes, height to the ground, volume). The findings hence confirming the hypothesis since the volume never had any influence as per requirements recommendations by Pinkalski et al (2015). 25.	Pinkalski, C.; Damgaard, C.; Jensen, K.V.; Gislum, R.; Peng, R.; Offenberg, J. Non-destructive biomass estimation of Oecophylla smaragdina colonies: a model species for the ecological impact of ants. Insect Conservation and Diversity, 2015, 8(5), 464–473. https://doi.org/10.1111/icad.12126.  

2. The tables record of occupied and unoccupied palm trees show the average % of unoccupied palm tree per colony. This information is important to estimate the population size-density in large plantations (500 ha or more).
DOI: 10.17632/5xf2ykkmr3.3
title: Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Formicidae: Formicinae) and it’s dynamic in oil palm plantations industry

description: 1. An estimation of the Asian weaver ant population size (total workers: major and minor) by nest count method was performed in oil palm plantations. The hypothesis is that the total number of nests per colony multiplied by the average total workers in nests can provide an estimate of its population in a specific occupied area. These dataset analysis results shows that there is no relation between the nest distribution contents of individuals (major workers, minor workers, winged queens representing the population size-density) and the determination of nests characteristic parameters (sizes, height to the ground, volume). The findings hence confirming the hypothesis since the volume never had any influence as per requirements recommendations by Pinkalski et al (2015). 25. Pinkalski, C.; Damgaard, C.; Jensen, K.V.; Gislum, R.; Peng, R.; Offenberg, J. Non-destructive biomass estimation of Oecophylla smaragdina colonies: a model species for the ecological impact of ants. Insect Conservation and Diversity, 2015, 8(5), 464–473. https://doi.org/10.1111/icad.12126. 

2. The tables record of occupied and unoccupied palm trees show the average % of unoccupied palm tree per colony. This information is important to estimate the population size-density in large plantations (500 ha or more). The dataset has been re-arranged.
DOI: 10.17632/5xf2ykkmr3.4
title: Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Formicidae: Formicinae) and it’s dynamic in oil palm plantations industry

description: 1. An estimation of the Asian weaver ant population size (total workers: major and minor) by nest count method was performed in oil palm plantations. The hypothesis is that the total number of nests per colony multiplied by the average total workers in nests can provide an estimate of its population in a specific occupied area. These dataset analysis results shows that there is no relation between the nest distribution contents of individuals (major workers, minor workers, winged queens representing the population size-density) and the determination of nests characteristic parameters (sizes, height to the ground, volume). The findings hence confirming the hypothesis since the volume never had any influence as per requirements recommendations by Pinkalski et al (2015). 25. Pinkalski, C.; Damgaard, C.; Jensen, K.V.; Gislum, R.; Peng, R.; Offenberg, J. Non-destructive biomass estimation of Oecophylla smaragdina colonies: a model species for the ecological impact of ants. Insect Conservation and Diversity, 2015, 8(5), 464–473. https://doi.org/10.1111/icad.12126. 

2. The tables record of occupied and unoccupied palm trees show the average % of unoccupied palm tree per colony. This information is important to estimate the population size-density in large plantations (500 ha or more). The dataset has been re-arranged.