cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14563574
title: Towards Transparency in Collaborative Mapping: Detecting Machine-Generated Roads in OpenStreetMap

description: The influx of machine-generated data, especially from Geospatial Artificial Intelligence (GeoAI), has grown significantly in less than a decade. AI-assisted mapping, a technological innovation leveraging open GeoAI data, integrates human validation to update crowdsourced databases, such as OpenStreetMap (OSM) is , creating a sense of security and quality assurance.  However, OSM contributors have expressed mixed feelings about the presence of AI content in the database, raising questions about their underlying fears and whether it is possible to differentiate between these data after integration. To explore this, first, we analyzed discussions among OSM contributors and identified concerns about data authenticity, quality, and the balance between human input and AI contributions.  Secondly, we  investigated the extent to which machine-generated roads can be detected within OSM. 

 

This data represent data used in our paper titled "Towards Transparency in Collaborative Mapping: Detecting Machine-Generated Roads in OpenStreetMap". It includes OSM roads data from GeoAI and OSM human constributors.
DOI: 10.15468/dl.f25rt5
title: Occurrence Download

description: A dataset containing 128639 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-7.0786 37.01273,-6.49958 35.6243,-3.74236 36.23826,-5.46562 37.95353,-7.0786 37.01273))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 128639 records from 455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050345-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7phtz8
title: Occurrence Download

description: A dataset containing 4087 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Grus nigricollis Prjevalsky, 1876"
 ]
}

 The dataset includes 4087 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050347-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14566163
title: Physical Properties of Agarose

description: This dataset compares the melting point and gelling point of agarose extracted from Gracilaria sp. with commercial agarose and other species. These properties are important for determining the thermal behavior and potential applications of the agarose
DOI: 10.5281/zenodo.14566164
title: Physical Properties of Agarose

description: This dataset compares the melting point and gelling point of agarose extracted from Gracilaria sp. with commercial agarose and other species. These properties are important for determining the thermal behavior and potential applications of the agarose
DOI: 10.15468/dl.jv88qs
title: Occurrence Download

description: A dataset containing 147775 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is BRA.7.1_2",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 147775 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050348-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14566173
title: FTIR Spectral Data

description: This dataset presents the FTIR spectra of agarose derived from Gracilaria sp. and commercial agarose. The identification of functional groups, such as hydroxyl, sulfate, and glycosidic bonds, provides insights into the chemical structure and purity of the extracted agarose
DOI: 10.5281/zenodo.14566174
title: FTIR Spectral Data

description: This dataset presents the FTIR spectra of agarose derived from Gracilaria sp. and commercial agarose. The identification of functional groups, such as hydroxyl, sulfate, and glycosidic bonds, provides insights into the chemical structure and purity of the extracted agarose
DOI: 10.15468/dl.5j8ejw
title: Occurrence Download

description: A dataset containing 1067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is COG.2.1_1",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 1067 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050349-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13441471
title: Rovibrationally resolved Rayleigh and Raman scattering cross sections for H2

description: Rayleigh and Raman scattering cross sections for all rovibrational transitions in the ground electronic state of molecular hydrogen. A total of 9582 cross sections for Rayleigh and Raman scattering of unpoalrized photons are given as a function of incident photon energy for energies less than 15 eV. Tensor components are also provided, which can be used to calculate integrated and differential cross sections. (Compressed = 2.31 GB, uncompressed = 9.81 GB) 

Supplementary materials to a manuscript under review
DOI: 10.5281/zenodo.13441472
title: Rovibrationally resolved Rayleigh and Raman scattering cross sections for H2

description: Rayleigh and Raman scattering cross sections for all rovibrational transitions in the ground electronic state of molecular hydrogen. A total of 9582 cross sections for Rayleigh and Raman scattering of unpoalrized photons are given as a function of incident photon energy for energies less than 15 eV. Tensor components are also provided, which can be used to calculate integrated and differential cross sections. (Compressed = 2.31 GB, uncompressed = 9.81 GB) 

Supplementary materials to a manuscript under review
DOI: 10.15468/dl.swmehh
title: Occurrence Download

description: A dataset containing 27545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is DEU.5_1",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 27545 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050350-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14566184
title: Sulfate Content Comparison

description: This dataset shows the sulfate content of agarose from Gracilaria sp. and compares it with commercial agarose and other agarose sources. Sulfate content plays a significant role in determining the gel strength and biocompatibility of agarose
DOI: 10.5281/zenodo.14566185
title: Sulfate Content Comparison

description: This dataset shows the sulfate content of agarose from Gracilaria sp. and compares it with commercial agarose and other agarose sources. Sulfate content plays a significant role in determining the gel strength and biocompatibility of agarose
DOI: 10.15468/dvut45
title: Checklist of vertebrates of Falz-Fein Biosphere Reserve "Askania Nova" (Kherson region, Ukraine)

description: This dataset presents a checklist of vertebrate fauna from the territory of the Falz-Fein Biosphere Reserve "Askania Nova" (natural core, zoo, dendrological park, buffer and interaction zones) situated in the Kherson region, Ukraine. The checklist is based on author's observations made during 2016–2022, as well as the observations made by the staff of the Reserve during 2010–2022.
The DarwinCore Archive of this resource consists of three tab-separated tables, connected by "taxonID" term: 
- core: taxonomic information;
- vernacular: species' vernacular names in Ukrainian;
- distribution: information on species' category in Ukrainian and international Red Lists, as well as the establishment status (native, captive, or both).
DOI: 10.5281/zenodo.14566190
title: Cara Cek Saman Guna No Plat: Panduan Mudah dan Cepat

description: Mungkin ramai yang tidak sedar bahawa kini anda boleh cek saman hanya menggunakan no plat kenderaan anda. Dengan teknologi digital yang semakin maju, proses ini menjadi lebih mudah dan pantas. Artikel ini akan menerangkan langkah-langkah mudah untuk memeriksa saman menggunakan nombor plat kenderaan, serta memberikan pautan berguna untuk memudahkan urusan anda.

Kenapa Penting untuk Cek Saman Secara Berkala?

Menyemak saman secara berkala penting untuk:



Mengelakkan penalti tambahan – Bayaran saman boleh meningkat jika lewat dijelaskan.

Mengelakkan masalah undang-undang – Saman yang tidak dibayar boleh menyebabkan lesen memandu anda digantung.

Memastikan rekod anda bersih – Saman yang tertunggak memberi kesan kepada laporan kredit atau urusan jual beli kenderaan.


Cara Cek Saman Guna No Plat

Berikut adalah langkah-langkah untuk menyemak saman menggunakan nombor plat kenderaan anda:





Lawati Portal RasmiPergi ke laman web rasmi seperti emasmy.org yang menawarkan pelbagai maklumat penting berkaitan kenderaan.




Masukkan Nombor PlatCari bahagian semakan saman dan masukkan nombor plat kenderaan anda. Pastikan nombor yang dimasukkan tepat untuk mendapatkan maklumat yang betul.




Pilih Jenis SemakanPilih jenis semakan saman yang ingin dilakukan, seperti saman trafik PDRM, JPJ, atau AES.




Tunggu Hasil SemakanDalam beberapa saat, semua saman yang berkaitan dengan nombor plat tersebut akan dipaparkan. Anda boleh memilih untuk mencetak maklumat tersebut atau terus ke proses pembayaran saman.



Cara Alternatif untuk Semak Saman

Selain laman web, anda juga boleh menggunakan:



Aplikasi mudah alih – Kebanyakan agensi kerajaan menyediakan aplikasi rasmi untuk semakan saman.

SMS – Gunakan kod khas yang disediakan oleh JPJ atau PDRM untuk mendapatkan maklumat saman.

Kiosk atau kaunter fizikal – Jika anda lebih selesa dengan kaedah manual, anda boleh ke kiosk JPJ atau PDRM berhampiran.


Manfaatkan Maklumat di emasmy.org

Laman web emasmy.org bukan sahaja menyediakan panduan semakan saman tetapi juga pelbagai informasi lain seperti cara semak pemilik kenderaan melalui no plat. Pastikan anda melawat laman ini untuk mendapatkan maklumat terkini dan lengkap. Dengan langkah mudah yang telah dikongsikan, anda kini boleh cek saman guna no plat tanpa masalah. Jangan lupa untuk membayar saman dengan segera bagi memastikan anda tidak menghadapi sebarang masalah di kemudian hari.
DOI: 10.5281/zenodo.14566191
title: Cara Cek Saman Guna No Plat: Panduan Mudah dan Cepat

description: Mungkin ramai yang tidak sedar bahawa kini anda boleh cek saman hanya menggunakan no plat kenderaan anda. Dengan teknologi digital yang semakin maju, proses ini menjadi lebih mudah dan pantas. Artikel ini akan menerangkan langkah-langkah mudah untuk memeriksa saman menggunakan nombor plat kenderaan, serta memberikan pautan berguna untuk memudahkan urusan anda.

Kenapa Penting untuk Cek Saman Secara Berkala?

Menyemak saman secara berkala penting untuk:



Mengelakkan penalti tambahan – Bayaran saman boleh meningkat jika lewat dijelaskan.

Mengelakkan masalah undang-undang – Saman yang tidak dibayar boleh menyebabkan lesen memandu anda digantung.

Memastikan rekod anda bersih – Saman yang tertunggak memberi kesan kepada laporan kredit atau urusan jual beli kenderaan.


Cara Cek Saman Guna No Plat

Berikut adalah langkah-langkah untuk menyemak saman menggunakan nombor plat kenderaan anda:





Lawati Portal RasmiPergi ke laman web rasmi seperti emasmy.org yang menawarkan pelbagai maklumat penting berkaitan kenderaan.




Masukkan Nombor PlatCari bahagian semakan saman dan masukkan nombor plat kenderaan anda. Pastikan nombor yang dimasukkan tepat untuk mendapatkan maklumat yang betul.




Pilih Jenis SemakanPilih jenis semakan saman yang ingin dilakukan, seperti saman trafik PDRM, JPJ, atau AES.




Tunggu Hasil SemakanDalam beberapa saat, semua saman yang berkaitan dengan nombor plat tersebut akan dipaparkan. Anda boleh memilih untuk mencetak maklumat tersebut atau terus ke proses pembayaran saman.



Cara Alternatif untuk Semak Saman

Selain laman web, anda juga boleh menggunakan:



Aplikasi mudah alih – Kebanyakan agensi kerajaan menyediakan aplikasi rasmi untuk semakan saman.

SMS – Gunakan kod khas yang disediakan oleh JPJ atau PDRM untuk mendapatkan maklumat saman.

Kiosk atau kaunter fizikal – Jika anda lebih selesa dengan kaedah manual, anda boleh ke kiosk JPJ atau PDRM berhampiran.


Manfaatkan Maklumat di emasmy.org

Laman web emasmy.org bukan sahaja menyediakan panduan semakan saman tetapi juga pelbagai informasi lain seperti cara semak pemilik kenderaan melalui no plat. Pastikan anda melawat laman ini untuk mendapatkan maklumat terkini dan lengkap. Dengan langkah mudah yang telah dikongsikan, anda kini boleh cek saman guna no plat tanpa masalah. Jangan lupa untuk membayar saman dengan segera bagi memastikan anda tidak menghadapi sebarang masalah di kemudian hari.
DOI: 10.5281/zenodo.14566192
title: Physical Properties of Nanocomposite Films

description: This dataset evaluates the thickness and solubility of nanocomposite films made from agarose, agarose + AgNPs, and agarose + CuNPs. These properties are critical for assessing the physical performance and stability of the films in various applications.
DOI: 10.5281/zenodo.14566193
title: Physical Properties of Nanocomposite Films

description: This dataset evaluates the thickness and solubility of nanocomposite films made from agarose, agarose + AgNPs, and agarose + CuNPs. These properties are critical for assessing the physical performance and stability of the films in various applications.
DOI: 10.5281/zenodo.14566208
title: Antibacterial Activity Data

description: This dataset categorizes the antibacterial activity of nanocomposite films based on inhibition zone diameters. The classification (weak, moderate, strong, or very strong) helps in determining the efficacy of the films against bacterial strains.
DOI: 10.5281/zenodo.14566209
title: Antibacterial Activity Data

description: This dataset categorizes the antibacterial activity of nanocomposite films based on inhibition zone diameters. The classification (weak, moderate, strong, or very strong) helps in determining the efficacy of the films against bacterial strains.
DOI: 10.21227/5aeg-0w65
title: Annual Load Dataset of Electric Vehicle Charging Stations

description: The charging load dataset are collected by a smart energy measurement system over a one-year period, with data recorded hourly from six Electric Vehicle Charging Stations (EVCSs) located in the city center of China. The data provides a full annual cycle of charging behaviors. The charging stations, labeled EVCS1 through EVCS6, have their loads reported in kilowatts (kW), with values recorded to four decimal places, ensuring high precision. Each row in the dataset includes a timestamp in the format of "YYYYMMDD," along with the charging load values for each EVCS, enabling a detailed analysis of temporal variations. This real-world dataset can be utilized for analyzing the operational patterns of EVCSs, optimizing charging infrastructure, and studying the impact of EV charging on the urban power grid.
DOI: 10.21227/rmc8-ve92
title: Annual Load Dataset of Electric Vehicle Charging Stations

description: The charging load dataset are collected by a smart energy measurement system over a one-year period, with data recorded hourly from six Electric Vehicle Charging Stations (EVCSs) located in the city center of China. The data provides a full annual cycle of charging behaviors. The charging stations, labeled EVCS1 through EVCS6, have their loads reported in kilowatts (kW), with values recorded to four decimal places, ensuring high precision. Each row in the dataset includes a timestamp in the format of "YYYYMMDD," along with the charging load values for each EVCS, enabling a detailed analysis of temporal variations. This real-world dataset can be utilized for analyzing the operational patterns of EVCSs, optimizing charging infrastructure, and studying the impact of EV charging on the urban power grid.
DOI: 10.5281/zenodo.14566046
title: paraliparis_nigrolineatus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566047
title: paraliparis_nigrolineatus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566222
title: paraliparis_nullansa_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566223
title: paraliparis_nullansa_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14564839
title: Optimizing E-Commerce User Experiences Investigating Chatbot Effectiveness and Behavioral Intention Through the Technology Acceptance Model

description: Dataset
DOI: 10.5281/zenodo.14564840
title: Optimizing E-Commerce User Experiences Investigating Chatbot Effectiveness and Behavioral Intention Through the Technology Acceptance Model

description: Dataset
DOI: 10.5281/zenodo.14566227
title: paraliparis_orbitalis_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566228
title: paraliparis_orbitalis_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.xbwmzj
title: Occurrence Download

description: A dataset containing 456897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Oceania",
 "Country is Australia",
 "Geometry POLYGON((151.1822 -33.98217,151.27509 -33.98161,151.28383 -33.87985,151.21739 -33.88112,151.15185 -33.87899,151.16506 -33.9365,151.1822 -33.98217))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is CC-BY 4.0",
 "OccurrenceStatus is Present",
 "Year 1990-2024"
 ]
}

 The dataset includes 456897 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050351-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/ta7e-tw66
title: Drosophila Extinction Learning Data

description: This dataset contains a collection of time series of graphs, each associated with a learning score. Each time series represents the evolution of a larval Drosophila connectome under certain conditions. Every network has one hundred and forty nodes, and each series has one hundred and sixty timepoints. At a certain timepoint, a stimulus is delivered along with pain, and then subsequently at two other timepoints only the stimulus is delivered without any pain. The learning score measures how well the organism has learnt to disassociate the stimuls from pain.
DOI: 10.15468/dl.subthu
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Mechanitis polymnia veritabilis Butler, 1873"
 ]
}

 The dataset includes 179 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050352-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14566291
title: paraliparis_parviradialis_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566292
title: paraliparis_parviradialis_decontaminated_FSCR.fasta.gz
DOI: 10.6084/m9.figshare.28099454
title: single-cell transcriptomic dataset of Treatment-naïve solid tumors

description: single-cell transcriptomic dataset of Treatment-naïve solid tumors<br>
DOI: 10.6084/m9.figshare.28099454.v1
title: single-cell transcriptomic dataset of Treatment-naïve solid tumors

description: single-cell transcriptomic dataset of Treatment-naïve solid tumors<br>
DOI: 10.6084/m9.figshare.25586856
title: Data for: Effect of exogenous treatment with zaxinone and its mimics on rice root microbiota across different growth stages

description: This databse contains data generated in the following study:Mazzarella, T., Chialva, M., de Souza, L.P. <i>et al.</i> Effect of exogenous treatment with zaxinone and its mimics on rice root microbiota across different growth stages. <i>Sci Rep</i> <b>14</b>, 31374 (2024). https://doi.org/10.1038/s41598-024-82833-6<b>16S_data.RData </b>/ <b>ITS2_data.RData</b>: R environments containing source meta-barcoding data for 16S (Prokaryotes) and ITS2 (Fungi) dataset, respectively. Each environment contains two phyloseq objects:<i>feature_phylos</i>: raw count matrix (ASVs table) with contaminants already removed using decontam package using blank samples<br><i>feature_phylos_filtered_DESeq</i>: ASVs table normalized using DESeq2 normalized counts (median of ratios) on raw counts filtered by abundance with HTSfilter R package.<b>ASVphylo_DA.nwk</b>: phylogenetic tree of differentially abundant ASV in Zax and MiZax treatments <i>versus</i> the acetone control.<b>ASV.MSA_DA.fna</b>: sequence aligment used to reconstruct phylogeny of differentially abundant ASV in Zax and MiZax treatments <i>versus</i> the acetone control.
DOI: 10.6084/m9.figshare.25586856.v1
title: Data for: Effect of exogenous treatment with zaxinone and its mimics on rice root microbiota across different growth stages

description: This databse contains data generated in the following study:Mazzarella, T., Chialva, M., de Souza, L.P. <i>et al.</i> Effect of exogenous treatment with zaxinone and its mimics on rice root microbiota across different growth stages. <i>Sci Rep</i> <b>14</b>, 31374 (2024). https://doi.org/10.1038/s41598-024-82833-6<b>16S_data.RData </b>/ <b>ITS2_data.RData</b>: R environments containing source meta-barcoding data for 16S (Prokaryotes) and ITS2 (Fungi) dataset, respectively. Each environment contains two phyloseq objects:<i>feature_phylos</i>: raw count matrix (ASVs table) with contaminants already removed using decontam package using blank samples<br><i>feature_phylos_filtered_DESeq</i>: ASVs table normalized using DESeq2 normalized counts (median of ratios) on raw counts filtered by abundance with HTSfilter R package.<b>ASVphylo_DA.nwk</b>: phylogenetic tree of differentially abundant ASV in Zax and MiZax treatments <i>versus</i> the acetone control.<b>ASV.MSA_DA.fna</b>: sequence aligment used to reconstruct phylogeny of differentially abundant ASV in Zax and MiZax treatments <i>versus</i> the acetone control.
DOI: 10.5281/zenodo.14566349
title: paraliparis_paucidens_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14566350
title: paraliparis_paucidens_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.mv9qju
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bos mutus (Przewalski, 1883)"
 ]
}

 The dataset includes 36 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050353-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0004344
title: 论文附件数据集-《A SUPERMAN-like Gene Controls the Locule Number of Tomato Fruit》
DOI: 10.57841/casdc.0004345
title: 论文附件数据集-《A large-scale behavior of allelic dropout and imbalance caused by DNA methylation changes in an early-ripening bud sport of peach》
DOI: 10.57841/casdc.0004346
title: 论文附件数据集-《Comprehensive Analysis of Metabolome and Transcriptome in Fruits and Roots of Kiwifruit》
DOI: 10.5281/zenodo.14566343
title: Flood preparedness Malaysia
DOI: 10.5281/zenodo.14566344
title: Flood preparedness Malaysia
DOI: 10.57841/casdc.0004347
title: 论文附件数据集-《Genome-Wide Identification and Expression Analysis of Polygalacturonase Gene Family in Kiwifruit (Actinidia chinensis) during Fruit Softening》
DOI: 10.5281/zenodo.14566337
title: Sdoppiamento contratto negli annunci di lavoro: lista di prompt, large language model e risposte generate
DOI: 10.5281/zenodo.14566338
title: Sdoppiamento contratto negli annunci di lavoro: lista di prompt, large language model e risposte generate
DOI: 10.57841/casdc.0004349
title: 论文附件数据集-《Identiﬁcation of EIL and ERF Genes Related to Fruit Ripening in Peach》