cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/syp68bvdc6.1
title: supplymetary material to "Assessment of Long-Read Sequencing-based Congenital Adrenal Hyperplasia Genotyping Assay for Newborns in Fujian, China"

description: supplymetary material to "Assessment of Long-Read Sequencing-based Congenital Adrenal Hyperplasia Genotyping Assay for Newborns in Fujian, China"
DOI: 10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2
title: Code underlying publication: Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

description: This collection contains all code to produce the results of "Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation," <em style="color:rgb(51, 51, 51);">2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>, Prague, Czech Republic, 2021, pp. 6854-6859, doi: 10.1109/IROS51168.2021.9635963. This paper leverages reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensormeasurements. The code is written in python. To use the code, the readers could set up the Python environment according to "requirements.txt." For details, please follow "README.md".
DOI: 10.4121/9da2c9da-9031-4b02-8c01-04f47494afd2.v1
title: Code underlying publication: Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation

description: This collection contains all code to produce the results of "Reinforcement Learning Compensated Extended Kalman Filter for Attitude Estimation," <em style="color:rgb(51, 51, 51);">2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</em>, Prague, Czech Republic, 2021, pp. 6854-6859, doi: 10.1109/IROS51168.2021.9635963. This paper leverages reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensormeasurements. The code is written in python. To use the code, the readers could set up the Python environment according to "requirements.txt." For details, please follow "README.md".
DOI: 10.26300/nxgn-ca56
title: Data documentation for Cascades Analytic Plan
DOI: 10.17632/xwgkxbr5sc.1
title: Semi-structured Interview between Interviewee and Interviewer regarding Perspectives of athletes on rehabilitation after ACL reconstruction

description: Perspectives of ten football players regarding rehabilitation after Anterior Cruciate Ligament (ACL) reconstruction were explored through the semi-structured interview guide administered telephonically.
DOI: 10.17632/rzjww3vdxt.1
title: Advanced Dataset on Money Plant Diseases for AI Pathology Research

description: 1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (72 images), Bacterial Wilt Disease (66 images), and Healthy (175) images. These classes represent both damaged and healthy leaves. The dataset has 313 photos in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 313 photos depicting Bacterial Wilt Disease (66), Healthy (175), and Manganese Toxicity (72) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 15,000 augmented images are made from these original photos in order to increase the quantity of data sets.
DOI: 10.17632/rzjww3vdxt.2
title: Advanced Dataset on Money Plant Diseases for AI Pathology Research

description: 1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (700 images), Bacterial Wilt Disease (700 images), and Healthy (700) images. These classes represent both damaged and healthy leaves. The dataset has 2100 images in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 2100 images depicting Bacterial Wilt Disease (700), Healthy (700), and Manganese Toxicity (700) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 15,000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.15468/dl.834ntx
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dactylorhiza majalis subsp. calcifugiens H.A.Pedersen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 83 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010763-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4gdpmmk2yy.1
title: Household Food waste in Delhi, India

description: The data pertains to a physical waste survey undertaken in Urban areas in South Delhi District, Delhi, India. The physical waste survey was undertaken to quantify avoidable food waste (AFW) and explore and understand the drivers of AFW at the household level. Household waste was collected for seven consecutive days from each of the 56 households. Subsequently, one participant from each of the 17 high AFW-generating households was engaged in a semi-structured interview to uncover the drivers of household AFW. A mixed methods approach was employed to understand the phenomenon of AFW in urban households. Descriptive and inferential analysis was undertaken on the physical waste data. The interview data was analyzed through thematic analysis. The following data files are attached
1. Physical waste survey data
2. Food categories: The FW items were categorized into 14 food categories. 
3. Participant and household data (Anonymized) 
4. Semi-structured interview guide/protocol
DOI: 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4
title: code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

description: This collection contains all code to produce the results of "Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee," <em style="color:rgb(51, 51, 51);">2021 IEEE International Conference on Robotics and Automation (ICRA)</em>, Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. To the best of our knowledge, this is the first DRL-based orientation estimation method using inertial sensors combined with a magnetometer. The code is written in Python. The packages used are listed in "requirements.txt". To reproduce the code, please refer to "README.md".
DOI: 10.4121/71bb6fd6-0983-442c-a266-fe3b7bee77e4.v1
title: code underlying publication: Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

description: This collection contains all code to produce the results of "Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee," <em style="color:rgb(51, 51, 51);">2021 IEEE International Conference on Robotics and Automation (ICRA)</em>, Xi'an, China, 2021, pp. 10243-10249, doi: 10.1109/ICRA48506.2021.9561440. This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. To the best of our knowledge, this is the first DRL-based orientation estimation method using inertial sensors combined with a magnetometer. The code is written in Python. The packages used are listed in "requirements.txt". To reproduce the code, please refer to "README.md".
DOI: 10.17632/wtfw7w4jhh.1
title: THE INDUS SCRIPT: Recognition as an Alphabet

description: This paper introduces a groundbreaking approach to deciphering the Indus script, employed by the Indus Valley Civilization (IVC) from c. 3300 to 1900 BCE. Moving beyond previous methods, my research delves into the intricate structure of the script's signs, aiming to definitively settle the debate regarding its categorization. Specifically, I investigate whether the Indus script functions as an alphabet, presenting compelling evidence for the number of primary signs it comprises. My methodology meticulously examines over 400 signs, encompassing the entire corpus rather than relying on limited samples. This holistic approach utilizes a novel gridbased decomposition technique to visually dissect compound signs, identify diacritics with consistent usage patterns, and uncover the underlying principles of sign extraction. Through this rigorous analysis, I successfully isolate the essential primary signs and illuminate their potential phonetic representations within an alphabetic system. However, I acknowledge the intricate variations in writing styles present across the Indus corpus. These stylistic subtleties, encompassing formation, composition, and combination strategies, contribute to the apparent plurality of signs. Nonetheless, my findings reveal a core set of only 40 fundamental signs, suggesting a remarkably concise system beneath the surface complexity. By presenting this new paradigm for understanding the Indus script, I pave the way for further advances in its decipherment. Continued exploration of the identified core signs and their phonetic associations holds immense potential for unlocking the linguistic insights encoded within this enigmatic script, shedding light on a crucial chapter in human history.
DOI: 10.18712/nsd-nsd1727-v2
title: Students' Understanding and Use of Scientific Knowledge, 1997

description: The purpose of "Students' Understanding and Use of Scientific Knowledge, 1997" was to increase insight in students' use of scientific knowledge and information when facing controversies of a scientific dimension, as well as looking at students' evaluation of the reliability of different types of scientific information. 
The background for the project was the increase in emphasizing general education in schools' curriculums for science. There was also little knowledge on how students evaluated and made use of scientific information in their daily life. 
To gain more insight in the above-mentioned problems, 22 in-depth interviews were carried out with students attending upper secondary school, during the period of January to June 1997. 
The main findings in the project were related to different strategies for evaluation of trustworthiness of information, related to a special context in the form of the controversy related to the possible health risks of growing up close to high-tension wires. Another main finding was related to models for how different information and different values affected different students' stands in the same controversial case.
DOI: 10.18712/nsd-nsd2154-v2
title: Hydrogen Storage in Novel Boron-Based Compounds, 2013

description: One major unsolved problem for the introduction of hydrogen as an energy carrier in the global energy system is effective storage of hydrogen. Complex hydrides based on boron are promising with hydrogen contents above 10 wt% hydrogen. Key issues addressed in the project are synthesis and characterization of novel alkali-transition metal borohydrides, often named mixed borohydrides. A breakthrough will in particular be very important for vehicle applications.

Data are presented in several publications and is available through various journals sites.
DOI: 10.5281/zenodo.14008114
title: CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis

description: This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well.
DOI: 10.5281/zenodo.14008115
title: CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis

description: This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well.
DOI: 10.15468/dl.qnb3m3
title: Occurrence Download

description: A dataset containing 129526 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycopodium L.",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 129526 records from 1097 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010748-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ehxg6
title: Occurrence Download

description: A dataset containing 20378 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eurygaster Laporte, 1833"
 ]
}

 The dataset includes 20378 records from 344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0173595-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rstwfv
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Indigofera giessii A.Schreib."
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0253149-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4aasnk
title: Occurrence Download

description: A dataset containing 21681 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aelia acuminata (Linnaeus, 1758)"
 ]
}

 The dataset includes 21681 records from 352 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0173597-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8g8hdf
title: Occurrence Download

description: A dataset containing 5220 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ligularia sibirica (L.) Cass."
 ]
}

 The dataset includes 5220 records from 217 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009265-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6gdh63
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-91.37118 41.74067,-86.65521 41.26907,-85.39762 39.53988,-83.82563 39.38268,-82.20124 37.60109,-81.52004 39.59228,-81.93924 41.42627,-83.08963 42.16297,-81.52004 43.57465,-79.55711 48.69331,-86.28841 45.46104,-91.37118 41.74067))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pyrrhia aurantiago Guenée, 1852"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0230443-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3d25g
title: Occurrence Download

description: A dataset containing 19929 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Global compendium of Aedes aegypti occurrence"
 ]
}

 The dataset includes 19929 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286572-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vgsuzd
title: Occurrence Download

description: A dataset containing 2249 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chenopodium L.",
 "InstitutionCode is BDBCV"
 ]
}

 The dataset includes 2249 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0173598-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5y7za
title: Occurrence Download

description: A dataset containing 557618 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sciurus vulgaris Linnaeus, 1758"
 ]
}

 The dataset includes 557618 records from 1516 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0204422-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14266974
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.48323/0e4ra-wf143
title: Reactivation of the silenced BASP1 gene suppresses [...]

description: Proteome Analysis

For proteome analysis of the SW480-B and SW480-V cell lines, proteins were obtained from simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE), with the protein interphase being dissolved in the urea-containing buffer. For proteomics, sample purification by reversed phase solid phase extraction (RP-SPE) and consecutive proteome analysis by LC-MS/MS was performed using a nanoUHPLC System (Dionex UltiMate 3000 RSLCnano pro flow, Thermo Fisher Scientific) coupled to an orbitrap tribrid mass spectrometer (Orbitrap Fusion Lumos, Thermo Fisher Scientific) as described. LC-MS/MS raw data were processed using Thermo Scientific Proteome Discoverer 2.4 SP1.
DOI: 10.48323/tgr9z-fn753
title: Reactivation of the silenced BASP1 gene suppresses [...]

description: Proteome Analysis

For proteome analysis of the SW480-B and SW480-V cell lines, proteins were obtained from simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE), with the protein interphase being dissolved in the urea-containing buffer. For proteomics, sample purification by reversed phase solid phase extraction (RP-SPE) and consecutive proteome analysis by LC-MS/MS was performed using a nanoUHPLC System (Dionex UltiMate 3000 RSLCnano pro flow, Thermo Fisher Scientific) coupled to an orbitrap tribrid mass spectrometer (Orbitrap Fusion Lumos, Thermo Fisher Scientific) as described. LC-MS/MS raw data were processed using Thermo Scientific Proteome Discoverer 2.4 SP1.
DOI: 10.15468/dl.pn8ss8
title: Occurrence Download

description: A dataset containing 1521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((4.07452 43.7335,4.2349 43.74966,4.24008 43.8572,4.06698 43.84761,4.07452 43.7335))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 1521 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063811-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5e4t68
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galium viridiflorum)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 59 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021597-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7pb4vc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus spinifex",
 "Country is Moldova, Republic of",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021598-250914085247600/datasets/export for details.
DOI: 10.15468/dl.t88a7c
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus spinifex",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021599-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.