cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14011569
title: A monthly 5 km Arctic sea ice thickness product from 1995 to 2023 using multiple radar altimetry data

description: Arctic sea ice is of great importance to the regional and global climate change study. Satellite observations have demonstrated that the Arctic sea ice extent has been declined for the last four decades. However, long-term variations of the Arctic sea ice thickness (SIT) are less focused as SIT cannot be measured directly by satellite-based instruments. Here, we presented a monthly Arctic SIT product based on multiple radar altimetry observations from ERS-2, Envisat, and CryoSat-2. To ensure the accuracy of the SIT retrievals, we proposed a novel data processing procedure including leads detection, freeboard conversion to thickness and inter-mission bias correction. Finally, we were able to generate the monthly SIT estimates for the Arctic Ocean from October 1995 to December 2023. The thickness estimates are posted on a 5 km resolution polar stereographic grid. The variations of the Arctic SIT are analyzed in terms of the spatial and temporal distributions. We also compared our SIT estimates with observations from upward looking sonars (ULSs) and airborne laser altimetry from Operation IceBridge (OIB), as well as seven publicly released Arctic SIT products. The validation results demonstrate that our SIT product features equivalent accuracy with existing products. The accuracy of our products is about 0.4 m during Envisat period, and is within 0.2 m during CryoSat-2 period.
DOI: 10.5281/zenodo.14969781
title: A monthly 5 km Arctic sea ice thickness product from 1995 to 2023 using multiple radar altimetry data

description: Arctic sea ice is of great importance to the regional and global climate change study. Satellite observations have demonstrated that the Arctic sea ice extent has been declined for the last four decades. However, long-term variations of the Arctic sea ice thickness (SIT) are less focused as SIT cannot be measured directly by satellite-based instruments. Here, we presented a monthly Arctic SIT product based on multiple radar altimetry observations from ERS-2, Envisat, and CryoSat-2. To ensure the accuracy of the SIT retrievals, we proposed a novel data processing procedure including leads detection, freeboard conversion to thickness and inter-mission bias correction. Finally, we were able to generate the monthly SIT estimates for the Arctic Ocean from October 1995 to December 2023. The thickness estimates are posted on a 5 km resolution polar stereographic grid. The variations of the Arctic SIT are analyzed in terms of the spatial and temporal distributions. We also compared our SIT estimates with observations from upward looking sonars (ULSs) and airborne laser altimetry from Operation IceBridge (OIB), as well as seven publicly released Arctic SIT products. The validation results demonstrate that our SIT product features equivalent accuracy with existing products. The accuracy of our products is about 0.4 m during Envisat period, and is within 0.2 m during CryoSat-2 period.
DOI: 10.5281/zenodo.14970240
title: Vulnerable Captchas

description: Description:






This dataset focuses on an interesting example of weak Vulnerable Captchas implementations, highlighting potential security vulnerabilities in systems that rely on simple alphanumeric captchas. CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are widely used to protect websites from bots and automated scripts. However, not all CAPTCHA implementations are equally secure, and some are prone to exploitation through automated processes.







Download Dataset









Context

The inspiration for this dataset came from a personal experience while accessing a website I frequently use, which I will refer to as “System” for privacy reasons. I wanted to automate a repetitive task on the site using a Python script, but I was initially blocked by a CAPTCHA that was required to complete the login process. CAPTCHAs are generally effective in stopping bots, especially those like Google’s reCAPTCHA, which are difficult to bypass with machine learning models due to their sophisticated design.

However, in this case, the CAPTCHA images were simple enough for human eyes to decipher, consisting only of clearly readable alphanumeric characters. The challenge intrigued me, and as I was simultaneously reading “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, I decided to use this scenario as an opportunity to apply my newly acquired knowledge in machine learning.

Problem and Approach

The dataset captures images of these vulnerable CAPTCHA challenges and provides annotations for each. During the process of automating the CAPTCHA resolution, I learned that the system did not just rely on the image itself. Upon inspection of the HTML, I found that the CAPTCHA content was hashed and stored inside a hidden form field. Which could easily be manipulate to bypass the verification entirely.

Key Learnings



CAPTCHA Design Matters: Not all CAPTCHAs are created equal. Simpler alphanumeric CAPTCHAs can often be defeated by image recognition models or form manipulation.

Image Classification: This dataset offers a collection of label CAPTCHA images.  That could be use to train image classification models aimed at recognizing and solving CAPTCHAs automatically.

Security Implications: The project sheds light on the importance of implementing proper security mechanisms beyond just CAPTCHA images.  Such as encryption, hashing, and verification strategies that prevent easy manipulation.

Practical Approach: Sometimes, simpler solutions such as analyzing the webpage structure and finding security loopholes can be more efficient than complex machine learning models.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14970241
title: Vulnerable Captchas

description: Description:






This dataset focuses on an interesting example of weak Vulnerable Captchas implementations, highlighting potential security vulnerabilities in systems that rely on simple alphanumeric captchas. CAPTCHAs (Completely Automated Public Turing Test to Tell Computers and Humans Apart) are widely used to protect websites from bots and automated scripts. However, not all CAPTCHA implementations are equally secure, and some are prone to exploitation through automated processes.







Download Dataset









Context

The inspiration for this dataset came from a personal experience while accessing a website I frequently use, which I will refer to as “System” for privacy reasons. I wanted to automate a repetitive task on the site using a Python script, but I was initially blocked by a CAPTCHA that was required to complete the login process. CAPTCHAs are generally effective in stopping bots, especially those like Google’s reCAPTCHA, which are difficult to bypass with machine learning models due to their sophisticated design.

However, in this case, the CAPTCHA images were simple enough for human eyes to decipher, consisting only of clearly readable alphanumeric characters. The challenge intrigued me, and as I was simultaneously reading “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, I decided to use this scenario as an opportunity to apply my newly acquired knowledge in machine learning.

Problem and Approach

The dataset captures images of these vulnerable CAPTCHA challenges and provides annotations for each. During the process of automating the CAPTCHA resolution, I learned that the system did not just rely on the image itself. Upon inspection of the HTML, I found that the CAPTCHA content was hashed and stored inside a hidden form field. Which could easily be manipulate to bypass the verification entirely.

Key Learnings



CAPTCHA Design Matters: Not all CAPTCHAs are created equal. Simpler alphanumeric CAPTCHAs can often be defeated by image recognition models or form manipulation.

Image Classification: This dataset offers a collection of label CAPTCHA images.  That could be use to train image classification models aimed at recognizing and solving CAPTCHAs automatically.

Security Implications: The project sheds light on the importance of implementing proper security mechanisms beyond just CAPTCHA images.  Such as encryption, hashing, and verification strategies that prevent easy manipulation.

Practical Approach: Sometimes, simpler solutions such as analyzing the webpage structure and finding security loopholes can be more efficient than complex machine learning models.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14970257
title: On the Different Levels of Understanding Boolean Expressions
DOI: 10.5281/zenodo.14970258
title: On the Different Levels of Understanding Boolean Expressions
DOI: 10.5281/zenodo.14970265
title: Predicted shear wave velocities of soils in Budapest

description: The database contains raw CPT and in situ seismeic measurement data together with shear wave velocity values predicted by different empirical correlations.
DOI: 10.5281/zenodo.14970266
title: Predicted shear wave velocities of soils in Budapest

description: The database contains raw CPT and in situ seismeic measurement data together with shear wave velocity values predicted by different empirical correlations.
DOI: 10.21227/s8c4-9g65
title: Multi-temporal UAV RGB image datasets for individual tree segmentation

description: This dataset comprises UAV-acquired RGB image samples covering three distinct forest ecosystem types across multiple phenological seasons. Each dataset package contains high-resolution PNG-format aerial imagery paired with corresponding annotation files, maintaining consistent filenames between images and their pixel-level vegetation labels for seamless data association. The time-series acquisition strategy captures seasonal variations in canopy structure, coloration, and density within identical geographic coordinates. Researchers can perform cross-seasonal comparative analysis by pairing summer and autumn/winter images from the same forest stand, enabling multi-temporal object detection of individual tree crowns with changing environmental conditions. This resource facilitates algorithm development for precision forestry, ecological monitoring, and climate change impact studies, particularly benefiting models requiring temporal feature learning from limited spectral (RGB) data. The standardized structure ensures compatibility with mainstream deep learning frameworks for vegetation remote sensing applications.
DOI: 10.15151/esrf-es-2044024984
title: Fast Access at Beamline ID19: Microtomography ("resubmission")

description: The experimental programme at beamline ID19 underwent tremendous changes in the more than 20 years of operation: starting from Bragg diffraction
topography (already with a substantial contribution of in situ experiments), the beamline pioneered hard X-ray phase contrast microtomography in routine user
application, later coupled with polychromatic illumination to exploit hard X-rays up to 250 keV as well as short exposure times. Here, we propose an access route
for routine microtomography which is fast, i.e. can be made available on short terms, and requires minimal overheads for the beamline staff. The basic idea is to
deploy the robotic sample changer of the beamline which has already proven to be a useful tool during the pandemic where users remotely scanned and changed
samples. It can be easily installed, with samples pre-mounted from the users loaded and made available in non-used evening and night shifts in a remote manner
exactly like it was done during the pandemic.
DOI: 10.15151/esrf-es-1975506322
title: Structural mechanisms of protein of biomedical and biotechnological application: pathological processes, targeted therapies, and enzyme desi

description: The Spanish Macromolecular Structural Groups in Granada, Almeria and Valencia present a continuation of their previous proposal (MX-2527) with the aim to form a strong core of scientists devoted to deciphering the structure and molecular mechanisms of proteins and protein complexes of biomedical and biotechnological interest. Among their objectives are deciphering the molecular basis of human pathologies and developing targeted therapies to designing new protein catalysts. This joint proposal follows the advice of the ESRF evaluation committee for merging two Teams with a long trajectory in protein crystallography and synchrotron usage, the Oriental Andalusian Team (Mx2454) led by JA Gavira and the Valencian Team with (Mx2452) led by S Ramón-Maiques. Based on the experience of the first year, we confirmed that this collaboration reinforces the crystallography teams and helps to better use synchrotron resources.
DOI: 10.15151/esrf-es-1975661101
title: Structural Biology at EMBL Grenoble, IBS-CIBB and IAB

description: This proposal clusters crystallography and bioSAXS projects at the EMBL Grenoble, IBS-CIBB and IAB. It combines groups working on different topics with a major focus on cell signaling proteins, drug discovery, long non-coding RNAs, nucleic acid-protein complexes, and host-pathogen interactions. The proposers have long-standing experience in the use, as well as the development of beam lines for macromolecular crystallography and biological small angle X-ray scattering and have participated in successive BAG applications over the years, mostly at the ESRF but also at other synchrotrons. The BAG responsible is Andrew McCarthy (EMBL Grenoble) for this application round.
DOI: 10.15151/esrf-es-2010924489
title: The Israeli BAG

description: This Summary will only portray a very limited scope of the BAG proposal due to its size and more information will be available in the Global Summary. We plan establish a comprehensive structural narrative for the assembly and entry of viruses from the Bunyavirales family using X-ray crystallography, biophysics, biochemistry, and molecular virology. Using X-ray crystallography to obtain the 3D structure of GN and GC in different conformations and from different bunyavituses. We will pursue a structure of GN or GC bound to a neutralizing antibody to obtain new data for vaccine design. In the SAXS arena, we plan to continue the study on MT (microtubules) and expand the experiments with different nucleotide type and concentrations, (GTP, GDP, GMPCPP, and GMPPCP, GTP-gammaS). We will perform at selected points SEC-SAXS experiments in the presence of full-length tau (4RL). Our preliminary data revealed that 4RL promote MT assembly at 36C and of single tubulin rings at low temperatures.
DOI: 10.15151/esrf-es-2010927774
title: IBS BAG

description: The IBS BAG includes teams from IBS (Grenoble) and external groups from CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study the structure-function relationships of proteins with a focus on human health, biotechnology and methodology. Most of the projects are a continuation of the MX-2586 proposal and can be grouped under the following themes: host-pathogen interaction, new therapeutic targets, fundamental cellular mechanisms, membrane proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, cryo-EM, SAXS and NMR) together with other biophysical and biochemical studies and, more recently, the use of AI (AlphaFold) to fully understand the complexity of the biological macromolecular systems under study.
DOI: 10.15151/esrf-es-2090689437
title: preparation for following experiment

description: We will mount gas cilynders in the external cabinet and align the beamline
DOI: 10.15151/esrf-es-2092940585
title: Strain mapping in a quantum shuttling device

description: We will employ Scanning X-Ray Diffraction Microscopy to map the spatial strain distribution in the 8 nm Si/Si0.7Ge0.3 quantum
well (QW) layer of a a functional Quantum Bus for coherent electron shuttling in a position where an electrostatic blockade of electron
transport was observed. SXMD will determine weither the electric barrier is induced by a structural defect in the semiconductor layers,
defective gate electrodes or if it is not related to structural changes, i.e. cause by a point defect. The previous attempt was unsuccesful due to the weak intensity of diffraction from the 10 nm Si QW layer, we anticipate that the recently comissioned KB mirrors at ID01 will provide a focussed beam with increased flux, making this measurement feasible
DOI: 10.15151/esrf-es-1976053193
title: CryoEM BAG for Ile-de-France

description: We are a group of structural biologists based in Paris that would like to benefit from high-end data collection shifts at ESRF. Our research spans various life science fields: bacterial transporters (Biou, Broutin) and machineries (Bellinzoni, Gubellini, Mechaly, Petrella), nucleic acid research (Leulliot, Sauguet, Tisné), eukaryotic channels and receptors (Prevost, Venien-Bryan, Wolff), virus biology and vaccine development (Barba-Spaeth, Guardado Calvo). Our group is already active as a community and the local proximity allows us to meet regularly. We have good access to screening microscopes but would need data collection on 300kV systems to reach cutting-edge resolution.
DOI: 10.15151/esrf-es-2035851188
title: Diffuse scattering in metastable rutile-related V0.92O2

description: The aim of the proposal is to study disorder phenomena in single crystals of rutile-related metastable V0.92O2 grown at 10 GPa and 1373 K using diffuse scattering and the 3D-delta-PDF approach. We plan to (i) collect high quality 3D diffuse scattering data as a function of temperature, (ii) analyse the type of correlations (displacements, composition, or both) with the 3D-delta-PDF method, (iii) document the critical behaviour of the short-range correlations in the vicinity of the phase transition from the temperature evolution of characteristic features of diffuse scattering.
DOI: 10.5281/zenodo.14389291
title: Supplementary data for paper entitled: "Highly oxidized products from the atmospheric reaction of hydroxyl radicals with isoprene" (https://doi.org/10.1038/s41467-025-57336-1)

description:  




This datasets includes output data and Python scripts used for plotting in the paper named "Highly oxidized products from the atmospheric reaction of hydroxyl radicals with isoprene" by Berndt et al. (2025) and published in the journal of Nature Communications (https://doi.org/10.1038/s41467-025-57336-1).
DOI: 10.5281/zenodo.14389292
title: Supplementary data for paper entitled: "Highly oxidized products from the atmospheric reaction of hydroxyl radicals with isoprene" (https://doi.org/10.1038/s41467-025-57336-1)

description:  




This datasets includes output data and Python scripts used for plotting in the paper named "Highly oxidized products from the atmospheric reaction of hydroxyl radicals with isoprene" by Berndt et al. (2025) and published in the journal of Nature Communications (https://doi.org/10.1038/s41467-025-57336-1).
DOI: 10.15468/dl.xq2ckm
title: Occurrence Download

description: A dataset containing 6952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bauhinia purpurea L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 6952 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005776-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.afc4rp
title: Occurrence Download

description: A dataset containing 623 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bauhinia tomentosa L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 623 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005777-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28530848
title: Comparison of responses between Chatgpt and clinical pharmacist

description: The emergence of electronic databases and information technology has reshaped drug information (DI) is accessed and provided. Generative Pre-trained Transformer (GPT)-4, has drawn attention for its potential application in clinical decision-making and medical research. Thus, the study compares responses from GPT-4 to those of clinical pharmacists in handling DI requests. Sixty DI requests based on common diseases in the UAE involving queries of different complexities were developed. Responses from GPT-4 were compared to those clinical pharmacists using cosine similarity method. Independent t-tests were used to compare them for accuracy and relevance. Two-way ANOVA was used to test the interaction between case complexities and language used in study outcomes. The mean similarity between pharmacists and GPT-4 was 0.88 (SD=0.05), with more than 80% cases almost identical. GPT-4 was as accurate as humans (4.23 vs. 4.13; P=.56). However, answers from GPT-4 were more comprehensive and patient-centered (4.25 vs. 3.72; P=.0006 and 4.35 vs. 3.75; P=.0008; respectively). GPT-4 had a significantly longer responses (270.2 vs. 150.3; P&lt;.0001) and required less time, but pharmacists used more relevant and up-to-date references. GPT-4 is a reliable tool for obtaining DI, but the DI skills of clinical pharmacists need to be improved.
DOI: 10.6084/m9.figshare.28530848.v1
title: Comparison of responses between Chatgpt and clinical pharmacist

description: The emergence of electronic databases and information technology has reshaped drug information (DI) is accessed and provided. Generative Pre-trained Transformer (GPT)-4, has drawn attention for its potential application in clinical decision-making and medical research. Thus, the study compares responses from GPT-4 to those of clinical pharmacists in handling DI requests. Sixty DI requests based on common diseases in the UAE involving queries of different complexities were developed. Responses from GPT-4 were compared to those clinical pharmacists using cosine similarity method. Independent t-tests were used to compare them for accuracy and relevance. Two-way ANOVA was used to test the interaction between case complexities and language used in study outcomes. The mean similarity between pharmacists and GPT-4 was 0.88 (SD=0.05), with more than 80% cases almost identical. GPT-4 was as accurate as humans (4.23 vs. 4.13; P=.56). However, answers from GPT-4 were more comprehensive and patient-centered (4.25 vs. 3.72; P=.0006 and 4.35 vs. 3.75; P=.0008; respectively). GPT-4 had a significantly longer responses (270.2 vs. 150.3; P&lt;.0001) and required less time, but pharmacists used more relevant and up-to-date references. GPT-4 is a reliable tool for obtaining DI, but the DI skills of clinical pharmacists need to be improved.