cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18773603
title: Supplementary code to "Algebraic surfaces as Hadamard products of curves"

description: README

How to use the code?

This repository contains Macaulay2 files and a Jupyter Notebook in Macaulay2.



In order to use the Jupyter Notebook: install Jupyter Notebook and add the Macaulay2 kernel, following the guide reported in the following GitHub page. Then, download the Jupyter Notebook file in the same folder used by Jupyter Notebook;

In order to use the Macaulay2 files: download the files in the same folder used by Macaulay2. If you want to use the functions and the results of the computation contained in the file sample.m2, you should use the command load "~/sample.m2".


What does this repository contain?



M2_Hadamard.ipynb: a Jupyter Notebook in Macaulay2 with all the computations related to the paper;

Hadamard_curves_P3.m2: a file containing a function that performs the Hadamard product of two curves L and R in 3-dimensional projective space defined by equations over the rationals. After loading this file, you can define the ideal I1 of L in the variables x0,...,x3, the ideal I2 of R in the variables y0,...,y3 and type X = preimage_Hadamard(I1+I2) to get the ideal of L \star R in the variables z0,...,z3;

example_3_3.m2: a file containing the computation related to Example 3.3 of the paper;

lemma_3_8.m2: a file containing the computation related to Lemma 3.8 of the paper;

example_4_6.m2: a file containing the computation related to Example 4.6 of the paper.
DOI: 10.15468/dl.r63ush
title: Occurrence Download

description: A dataset containing 10160 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera onca"
 ]
}

 The dataset includes 10160 records from 312 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000257-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/f47615b04b85a1c20dce5b1ee4fe3dba
title: Stream chemistry, flow, air temperature, and snowpack data from Cascade Brook at North Bridge in Black Rock Forest, Cornwall, NY, 1996 - 2008.

description: Black Rock Forest has monitored stream chemistry and flow in Cascade Brook through weekly measurements of pH, conductivity, air and water temperature, discharge, and snowpack from 1996 - 2008. The Cascade Brook watershed encompasses 135 hectares (334 acres), is generally a southern aspect, and has been little affected by human activity in the last century. The forest is deciduous and dominated by oak species. Together with precipitation data and samples from other streams in the forest, also collected weekly, investigators are better able to understand the the affects of acid precipitation and water dynamics in the forest.
DOI: 10.5281/zenodo.18773662
title: MODERN ACTIVE METHODS IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE.

description:  This thesis examines the contemporary active methods applied in teaching Russian as a Foreign Language (RFL), focusing on communicative, task-based, interactive, technology-enhanced, and learner-centered approaches that have transformed RFL pedagogy in the 21st century. It reviews theoretical foundations, teaching practices, empirical evidence of effectiveness, and pedagogical insights into how such methods improve learners’ communicative competence, motivation, and overall language acquisition.
DOI: 10.5281/zenodo.18773663
title: MODERN ACTIVE METHODS IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE.

description:  This thesis examines the contemporary active methods applied in teaching Russian as a Foreign Language (RFL), focusing on communicative, task-based, interactive, technology-enhanced, and learner-centered approaches that have transformed RFL pedagogy in the 21st century. It reviews theoretical foundations, teaching practices, empirical evidence of effectiveness, and pedagogical insights into how such methods improve learners’ communicative competence, motivation, and overall language acquisition.
DOI: 10.5281/zenodo.18773670
title: LINF_340012200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18773669
title: LINF_340012200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.15083648
title: Efficacy of robot-assisted gait training combined with conventional physiotherapy in postoperative patients with diparetic cerebral palsy: an observational study

description: This dataset is related to 27 post-surgical patients with diparetic CP  who underwent 15 sessions of robot-assisted gait training with the Lokomat system, combined with 15 sessions of conventional physiotherapy over 3 weeks.

The dataset includes subjects’ anagraphic and individual factors (such as age at recruitment, sex, GMFCS level, type of surgery and time from surgery prior to recrutiment); functional outcomes (i.e. the 6-minute walking test, the Gross Motor Function Measure-88 and the Gillette Functional Assessment Questionnaire), 3D gait analysis, spasticity and force metrics of the lower limb data collected before (T0) and after (T1) the treatment. 

In particular, the database is divided into several sheets related to : 



Anagraphic data

Clinical assessments


Columns B, C: 6MWT at T0 and T1;

Columns from D to O: GMFM-88 dimensions A,B,C,D,E and total score at T0 and T1;

Columns P, Q: FAQ measures at T0 and T1.



Gait parameters


Columns from B to AW: Spatiotemporal parameters collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from AX to CS: ankle kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from CT to DQ: knee kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from DR to GC: hip kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from GD to GS: trunk kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from GT to IK: kinetics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from IL to JY: Gait Variable Score and Gait Profile Score parameters collected at T0 and T1.



 Lokomat parameters


Columns from B to AW : Stiffness measurment at hip and kneel level at at three different velocities: •    hip: 22.5°/s, 45°/s, and 90°/s, peak angular velocity;•    knees: 30°/s, 60°/s and 120°/s, peak velocity

collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from AX to B: Isometric voluntary force produced in the hip and knee during flexion-extension movements collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs.

 

If you use this dataset please cite the following manucript: Falivene A, Biffi E, Molteni LE, Maghini C, Cima R, Morganti R, Diella E. Robot-Assisted Gait Training Combined with Conventional Physiotherapy in Postoperative Patients with Diplegic Cerebral Palsy: A Pilot Single Cohort Observational Study. Sensors. 2026; 26(5):1438. https://doi.org/10.3390/s26051438
description: The study was funded by the Italian Ministry of Health (Ricerca Corrente 2025/2026 to E. B.) and by the Italian Ministry of Research under the complementary actions to the NRRP  “Fit4MedRob - Fit for Medical Robotics” Grant (# PNC0000007)
DOI: 10.5281/zenodo.15083647
title: Efficacy of robot-assisted gait training combined with conventional physiotherapy in postoperative patients with diparetic cerebral palsy: an observational study

description: This dataset is related to 27 post-surgical patients with diparetic CP  who underwent 15 sessions of robot-assisted gait training with the Lokomat system, combined with 15 sessions of conventional physiotherapy over 3 weeks.

The dataset includes subjects’ anagraphic and individual factors (such as age at recruitment, sex, GMFCS level, type of surgery and time from surgery prior to recrutiment); functional outcomes (i.e. the 6-minute walking test, the Gross Motor Function Measure-88 and the Gillette Functional Assessment Questionnaire), 3D gait analysis, spasticity and force metrics of the lower limb data collected before (T0) and after (T1) the treatment. 

In particular, the database is divided into several sheets related to : 



Anagraphic data

Clinical assessments


Columns B, C: 6MWT at T0 and T1;

Columns from D to O: GMFM-88 dimensions A,B,C,D,E and total score at T0 and T1;

Columns P, Q: FAQ measures at T0 and T1.



Gait parameters


Columns from B to AW: Spatiotemporal parameters collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from AX to CS: ankle kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from CT to DQ: knee kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from DR to GC: hip kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from GD to GS: trunk kinematics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from GT to IK: kinetics collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from IL to JY: Gait Variable Score and Gait Profile Score parameters collected at T0 and T1.



 Lokomat parameters


Columns from B to AW : Stiffness measurment at hip and kneel level at at three different velocities: •    hip: 22.5°/s, 45°/s, and 90°/s, peak angular velocity;•    knees: 30°/s, 60°/s and 120°/s, peak velocity

collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs;

Columns from AX to B: Isometric voluntary force produced in the hip and knee during flexion-extension movements collected at T0 and T1 and divided for Impaired (I) and Less Impaierd (LI) legs.

 

If you use this dataset please cite the following manucript: Falivene A, Biffi E, Molteni LE, Maghini C, Cima R, Morganti R, Diella E. Robot-Assisted Gait Training Combined with Conventional Physiotherapy in Postoperative Patients with Diplegic Cerebral Palsy: A Pilot Single Cohort Observational Study. Sensors. 2026; 26(5):1438. https://doi.org/10.3390/s26051438
description: The study was funded by the Italian Ministry of Health (Ricerca Corrente 2025/2026 to E. B.) and by the Italian Ministry of Research under the complementary actions to the NRRP  “Fit4MedRob - Fit for Medical Robotics” Grant (# PNC0000007)
DOI: 10.34740/kaggle/dsv/14959516
title: All FDA Drugs (United States, 1939-Present)
DOI: 10.5281/zenodo.18714620
title: BRISC: A Dataset of Channel Measurements With a Reflective Intelligent Surface at 5 GHz

description: This repository contains:



antennaLog_posX.mat: refers to the CSI acquisition with the log-periodic antenna (X indicates the position index of the TX antenna)

antennaDipole_posX.mat:  refers to the CSI acquisition with the dipole antenna 

configurations_10000: the 10k RIS configurations used represented in hexadexial values. The 


The structure of the files is as follows:

-csi: an array of complex values with size [subcarrier_N x frame_N]. Specifically, frame_N is the number of total frames correctly processed at this position, while subcarrier_N is the number of subcarriers (242)

- confStr: an array of strings with size [frame_N x 1]. The i-th string indicates the configuration associated with the i-th extracted CSI.  

- confId: an array with size [1 x frame_N]. This is similar to confStr because the i-th element is associated with the i-th processed CSI. However, each element is a number between 1 and 10k, which positionally identifies the configuration from the `configurations_10000.mat`.

- sqn: an array indicating, for each processed CSI, the sequence counter extracted from the Wi-Fi frame. This is a value between 0 and 4095.  

- coarsets: reception timestamp.

---

Example

If you want to get all the CSI values associated with configuration number 1000, find the indices of all the elements in confId equal to 1000, and select the corresponding columns from the CSI matrix (each column is a CSI with 242 subcarriers).

 

Explanations relative to measurement setup, configurations choice etc. are available at:

 

@misc{piana2026briscdatasetchannelmeasurements,      title={BRISC: A Dataset of Channel Measurements at 5 GHz With a Reflective Intelligent Surface},       author={Mattia Piana and Giovanni Angelo Alghisi and Anna Valeria Guglielmi and Giovanni Perin and Francesco Gringoli and Stefano Tomasin},      year={2026},      eprint={2602.21102},      archivePrefix={arXiv},      primaryClass={eess.SP},      url={https://arxiv.org/abs/2602.21102}, }

 

Scripts used to process the data and attain the results in [1] are available at:

https://github.com/MattiaP1999/BRISC_RIS_dataset_scripts
DOI: 10.5281/zenodo.18714621
title: BRISC: A Dataset of Channel Measurements With a Reflective Intelligent Surface at 5 GHz

description: This repository contains:



antennaLog_posX.mat: refers to the CSI acquisition with the log-periodic antenna (X indicates the position index of the TX antenna)

antennaDipole_posX.mat:  refers to the CSI acquisition with the dipole antenna 

configurations_10000: the 10k RIS configurations used represented in hexadexial values. The 


The structure of the files is as follows:

-csi: an array of complex values with size [subcarrier_N x frame_N]. Specifically, frame_N is the number of total frames correctly processed at this position, while subcarrier_N is the number of subcarriers (242)

- confStr: an array of strings with size [frame_N x 1]. The i-th string indicates the configuration associated with the i-th extracted CSI.  

- confId: an array with size [1 x frame_N]. This is similar to confStr because the i-th element is associated with the i-th processed CSI. However, each element is a number between 1 and 10k, which positionally identifies the configuration from the `configurations_10000.mat`.

- sqn: an array indicating, for each processed CSI, the sequence counter extracted from the Wi-Fi frame. This is a value between 0 and 4095.  

- coarsets: reception timestamp.

---

Example

If you want to get all the CSI values associated with configuration number 1000, find the indices of all the elements in confId equal to 1000, and select the corresponding columns from the CSI matrix (each column is a CSI with 242 subcarriers).

 

Explanations relative to measurement setup, configurations choice etc. are available at:

 

@misc{piana2026briscdatasetchannelmeasurements,      title={BRISC: A Dataset of Channel Measurements at 5 GHz With a Reflective Intelligent Surface},       author={Mattia Piana and Giovanni Angelo Alghisi and Anna Valeria Guglielmi and Giovanni Perin and Francesco Gringoli and Stefano Tomasin},      year={2026},      eprint={2602.21102},      archivePrefix={arXiv},      primaryClass={eess.SP},      url={https://arxiv.org/abs/2602.21102}, }

 

Scripts used to process the data and attain the results in [1] are available at:

https://github.com/MattiaP1999/BRISC_RIS_dataset_scripts
DOI: 10.5281/zenodo.18773694
title: FUNCTIONAL ACTIVITY OF THE THYROID GLAND IN STRESS CONDITIONS

description: An analytical review was conducted of studies investigating the effects of acute and chronic stress on thyroid gland metabolism. Particular attention is given to the influence of stress and, specifically, glucocorticoids on thyroid status. The discussion encompasses stress-related alterations in iodine uptake, oxidation, and organification within thyrocytes, as well as changes in the peripheral metabolism of thyroid hormones, including their storage, transport, and tissue-specific deiodinase activity.The role of stress in the development of thyroid disorders is also examined, with emphasis on functional alterations of the thyroid gland associated with dysregulation of the hypothalamic–pituitary–adrenal axis. Emerging evidence suggests consistent patterns of thyroid dysfunction in conditions involving impaired pituitary–adrenal system activity.
DOI: 10.5281/zenodo.18773693
title: FUNCTIONAL ACTIVITY OF THE THYROID GLAND IN STRESS CONDITIONS

description: An analytical review was conducted of studies investigating the effects of acute and chronic stress on thyroid gland metabolism. Particular attention is given to the influence of stress and, specifically, glucocorticoids on thyroid status. The discussion encompasses stress-related alterations in iodine uptake, oxidation, and organification within thyrocytes, as well as changes in the peripheral metabolism of thyroid hormones, including their storage, transport, and tissue-specific deiodinase activity.The role of stress in the development of thyroid disorders is also examined, with emphasis on functional alterations of the thyroid gland associated with dysregulation of the hypothalamic–pituitary–adrenal axis. Emerging evidence suggests consistent patterns of thyroid dysfunction in conditions involving impaired pituitary–adrenal system activity.
DOI: 10.5281/zenodo.15386606
title: [Dataset] A dataset for 19th-century French land registry pages classification
title: [Dataset] Dataset pour la classification automatique de pages issues des registres d'états de sections du cadastre napoléonien

description: Initial registers pages classification dataset overview

This dataset is prepared to train classification models for French 19th-century land registry pages. It deals only with initial registers pages (états de sections in French). Images have been provided by the Val de Marne Archive and they can be associated with two former French departements : the Seine and the Seine-et-Oise (departements that were rounded Paris before 1968). There are three main layouts in these registers which are presents here.

The dataset counts 1016 pages and has been split into training, validation, and test sets with stratification based on the type of page.

It is structured according to YOLO Classification format.

The metadata file gives more information on the pages.

📊 Dataset Splits

| Split  | Size ||--------|------|| Train  | 745  || Val    | 123  || Test   | 148  |

---

🔠 Stratification by type of page 

Train Set

| Class              | Count ||--------------------|-------|| ets_tab_p1         | 413   || ets_recap_inter    | 198   || ets_tab_p2         | 89    || ets_resume         | 30    || ets_couv           | 15    |

Validation Set

| Class              | Count ||--------------------|-------|| ets_tab_p1         | 57    || ets_recap_inter    | 38    || ets_tab_p2         | 22    || ets_resume         | 3     || ets_couv           | 3     |

Test Set

| Class              | Count ||--------------------|-------|| ets_tab_p1         | 86    || ets_tab_p2         | 30    || ets_recap_inter    | 27    || ets_resume         | 4     || ets_couv           | 1     |

---

🏙️ Grouped by municipality

Communes are distinct across splits (useful to test geographic generalization), as land registry were produced for each municipality under the supervision of each departement.

Train Set

- VINCENNES : 76- IVRY : 72- GENTILLY : 68- VITRY : 60- LEPLESSIS : 52- SAINTMANDE : 48- NOGENT : 40- LHAY : 31- BRY : 28- JOINVILLE : 28- LEPERREUX : 24- ORMESSON : 24- MAROLLES : 24- MAISONSALFORT : 20- FRESNES : 20- CHARENTON : 20- CHOISY : 16- VILLIERS : 16- SAINTMAURICE : 12- VALENTON : 12- CRETEIL : 10- FONTENAY : 8- SANTENY : 8- SUCY : 8- VILLENEUVESTG : 8- BONNEUIL : 8- CHEVILLY : 4

Validation Set

- VILLEJUIF: 24- ARCUEIL: 20- RUNGIS: 20- MANDRES: 16- PERIGNY: 16- VILLECRESNES: 12- SAINTMAUR: 11- CHAMPIGNY: 4

Test Set

- ALFORTVILLE: 32- ORLY: 32- VILLENEUVELEROI: 24- THIAIS: 16- BOISSY: 12- LIMEIL: 12- NOISEAU: 12- ABLON: 8

---

🎨 Color Distribution

Indication on digitisation color of the images| Split  | COLOR | GREY ||--------|-------|------|| Train  | 473   | 272  || Val    | 40    | 83   || Test   | 64    | 84   |

---

📁 Usage

This dataset is intended for training a YOLO-v11 classification model.

---
DOI: 10.5286/isis.e.rb2520246-1
title: Characterizing complex structured neutron states

description: There has been rapid progress in the production and detection of structured neutron waves within the last 10 years. The ability to rapidly generate custom structured neutron input states to experiments for material characterization and fundamental physics is invaluable due to the tuneable additional degrees of freedom. Our goal is to initiate the widespread use of structured neutron beams at SANS beamlines. To achieve this, we plan to generate new structured neutron states at SANS2D using arrays of millions of micron sized diffraction gratings and exploit the TOF information for measuring large wavelength ranges in single measurements. In the proposed experiment we aim to characterize several structured neutron states such as neutron Airy vortex beams, neutron Airy beam self-healing and high order neutron OAM. Advancements in grating fabrication techniques have also made it possible to create high aspect ratio phase gratings for generating neutron OAM beams with significantly reduced acquisition times that we plan on testing and characterizing. These findings are expected to significantly advance our capabilities in generating complex structured neutron states that can be used as custom input states for material characterization experiments, exploiting additional degrees of freedom that have not been analyzed before.
DOI: 10.5286/isis.e.rb2520246
title: Characterizing complex structured neutron states

description: There has been rapid progress in the production and detection of structured neutron waves within the last 10 years. The ability to rapidly generate custom structured neutron input states to experiments for material characterization and fundamental physics is invaluable due to the tuneable additional degrees of freedom. Our goal is to initiate the widespread use of structured neutron beams at SANS beamlines. To achieve this, we plan to generate new structured neutron states at SANS2D using arrays of millions of micron sized diffraction gratings and exploit the TOF information for measuring large wavelength ranges in single measurements. In the proposed experiment we aim to characterize several structured neutron states such as neutron Airy vortex beams, neutron Airy beam self-healing and high order neutron OAM. Advancements in grating fabrication techniques have also made it possible to create high aspect ratio phase gratings for generating neutron OAM beams with significantly reduced acquisition times that we plan on testing and characterizing. These findings are expected to significantly advance our capabilities in generating complex structured neutron states that can be used as custom input states for material characterization experiments, exploiting additional degrees of freedom that have not been analyzed before.