cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29116997.v1
title: Pilot Testing the Youth Engaging Transition: Perspectives of Youth, Parents, and School Physical Therapists

description: To investigate the feasibility, acceptability, and relevance of the Youth Engaging Transition (YET) instrument. In this observational study, nine youth (16-21 years) with motor disabilities, their parents, and school physical therapists completed the YET, then triad members gave feedback about the experience (<i>n</i> = 27). Parents and PTs agreed that both sections (S1: Future Plans and S2: Self-Assessment and Goal Planning) were feasible. Three triads questioned the youth’s understanding of the YET. Parents agreed that S1 and S2 were acceptable. Three PTs disagreed that S2 was an appropriate scope/depth. Five triads questioned the YET’s length and repetitiveness. Youth and PTs reported that the YET was relevant. Perhaps due to previous transition planning experience, some parents reported not learning new information by completing the YET. Parents’ and PTs’ ratings were aligned, suggesting a common experience using the YET. Results of this pilot study suggest that the YET has promise as a feasible, acceptable, and relevant transition planning instrument. Larger studies investigating the validity and reliability of the YET should include modifications to the YET, involve other transition team members, and ascertain the team’s previous involvement in the planning process. Future YET iterations will include enhanced cognitive accessibility and shortened length.
DOI: 10.15468/dl.epm6rr
title: Occurrence Download

description: A dataset containing 237746 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Waterbird census in Spain",
 "Geometry POLYGON((-36.56491 24.47918,23.56491 24.47918,23.56491 48.52082,-36.56491 48.52082,-36.56491 24.47918))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 237746 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053532-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17605/osf.io/xphyn
title: Dramatherapy Literature Database

description: This project presents the most comprehensive collection of drama therapy literature to date in a downloadable format.
DOI: 10.15468/dl.a97hqz
title: Occurrence Download

description: A dataset containing 183316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -25.914722",
 "DecimalLatitude is less than or equal to -20.914722",
 "DecimalLongitude is greater than or equal to -55.0",
 "DecimalLongitude is less than or equal to -50.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 183316 records from 302 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053518-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17939085
title: Dataset for PS-LIBS on TiC nanoparticles
DOI: 10.5281/zenodo.17164374
title: Dataset for PS-LIBS on TiC nanoparticles
DOI: 10.17617/3.undkro
title: Stereotaxic brain atlas mole-rat Fukomys anselli

description: Plates of the histological brain atlas of the African mole-rat &lt;i&gt;Fukomys anselli&lt;/i&gt; with stereotaxic coordinates. Accompanying the manuscript "Neuronal recordings in head-fixed and freely-moving mole-rats".
DOI: 10.15468/dl.5hnq9z
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Swedish Malaise Trap Project (SMTP) - Psylloidea"
 ]
}

 The dataset includes 312 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053542-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4kmkaa
title: Occurrence Download

description: A dataset containing 3313 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Coptodon zillii"
 ]
}

 The dataset includes 3313 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053541-251120083545085/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.30884393
title: Datasets: Physical limits of sea-level rise adaptation in global river deltas

description: This Figshare repository includes two main datasets from the manuscript "Physical limits of sea-level rise adaptation in global river deltas" by Lasch et al. (pre-print can be found here). <b>SuppData1: Deltas Physical Characteristics </b>SuppData1 includes the physical characteristics for each of the 769 study deltas, with rows defined by unique BasinID2 identifiers. Variables include polygon area, projected sea-level rise, vertical land motion, river and sediment discharge, bathymetric slope, etc. sourced from existing global datasets. The dataset origins can be found in the manuscripts Supplementary Text (Section 3) and the code to run the geometric model using this dataset can be found in this manuscripts supporting CodeOcean capsule.<b>SuppData1: Mapped Physical Solution Spaces of Global Deltas</b>SuppData2 provides model outputs representing the physical solution space of adaptation per delta. The output is encoded as binary matrices (1 = physically feasible; 0 = infeasible). Code to generate the main manuscript figures is included in the model capsule (available through CodeOcean).
DOI: 10.6084/m9.figshare.30884393.v1
title: Datasets: Physical limits of sea-level rise adaptation in global river deltas

description: This Figshare repository includes two main datasets from the manuscript "Physical limits of sea-level rise adaptation in global river deltas" by Lasch et al. (pre-print can be found here). <b>SuppData1: Deltas Physical Characteristics </b>SuppData1 includes the physical characteristics for each of the 769 study deltas, with rows defined by unique BasinID2 identifiers. Variables include polygon area, projected sea-level rise, vertical land motion, river and sediment discharge, bathymetric slope, etc. sourced from existing global datasets. The dataset origins can be found in the manuscripts Supplementary Text (Section 3) and the code to run the geometric model using this dataset can be found in this manuscripts supporting CodeOcean capsule.<b>SuppData1: Mapped Physical Solution Spaces of Global Deltas</b>SuppData2 provides model outputs representing the physical solution space of adaptation per delta. The output is encoded as binary matrices (1 = physically feasible; 0 = infeasible). Code to generate the main manuscript figures is included in the model capsule (available through CodeOcean).
DOI: 10.15468/dl.yv5rrr
title: Occurrence Download

description: A dataset containing 16304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "GadmGid is IDN.31_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 16304 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053539-251120083545085/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.30884573
title: Raw outputs for Simulation Study 1: Number of Trials

description: The folders contain all the raw output of the first simulation study from the paper:<br>"Generalization in neural posterior estimation: Case studies with the racing diffusion model".<br><br>The structure of the folder is as follows:<br>- `experiment_1`: Study folder<br> - `rdm_*`: Study condition folder (e.g., rdm_simple refers to the default settings)<br> - `flow_matching`: Output related to neural posterior estimation (NPE)<br> - `checkpoints`: Includes a `model.keras` file that contains the checkpoint of the NPE<br> for this condition<br> - `metrics`: Aggregated metrics based on posterior samples from NPE and MCMC<br> - `npe_samples`: Raw posterior samples from NPE<br> - `mcmc_samples`: Raw posterior samples obtained from Markov chain Monte Carlo (MCMC)<br> - `test_data`: Test datasets<br><br>The names of the study conditions are as follows:<br><br>- `experiment_1`:<br> - `rdm_simple`: Default settings with trial number training range {250, 500, 750, 1000}<br> - `rdm_simple_discrete_lower`: Default settings with trial number training range [50, ..., 250]<br> - `rdm_simple_discrete_upper`: Default settings with trial number training range [1000, ..., 1200]<br> - `rdm_simple_discrete_lower`: Default settings with trial number training range [50, ..., 1200]<br>All datasets and posterior samples are stored in HDF5 files. Aggregated statistics are stored in CSV format.<br>
DOI: 10.6084/m9.figshare.30884573.v1
title: Raw outputs for Simulation Study 1: Number of Trials

description: The folders contain all the raw output of the first simulation study from the paper:<br>"Generalization in neural posterior estimation: Case studies with the racing diffusion model".<br><br>The structure of the folder is as follows:<br>- `experiment_1`: Study folder<br> - `rdm_*`: Study condition folder (e.g., rdm_simple refers to the default settings)<br> - `flow_matching`: Output related to neural posterior estimation (NPE)<br> - `checkpoints`: Includes a `model.keras` file that contains the checkpoint of the NPE<br> for this condition<br> - `metrics`: Aggregated metrics based on posterior samples from NPE and MCMC<br> - `npe_samples`: Raw posterior samples from NPE<br> - `mcmc_samples`: Raw posterior samples obtained from Markov chain Monte Carlo (MCMC)<br> - `test_data`: Test datasets<br><br>The names of the study conditions are as follows:<br><br>- `experiment_1`:<br> - `rdm_simple`: Default settings with trial number training range {250, 500, 750, 1000}<br> - `rdm_simple_discrete_lower`: Default settings with trial number training range [50, ..., 250]<br> - `rdm_simple_discrete_upper`: Default settings with trial number training range [1000, ..., 1200]<br> - `rdm_simple_discrete_lower`: Default settings with trial number training range [50, ..., 1200]<br>All datasets and posterior samples are stored in HDF5 files. Aggregated statistics are stored in CSV format.<br>
DOI: 10.15468/dl.j2jr46
title: Occurrence Download

description: A dataset containing 602 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Türkiye",
 "TaxonKey is Veronica persica"
 ]
}

 The dataset includes 602 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053544-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60895/redata/mzjkha
title: Breath-by-breath cardiometabolic data during walking and tango walk at self-selected speed

description: &lt;p&gt; This dataset contains structured records from individual cardiometabolic test sessions, extracted from original Excel files. Each entry corresponds to one subject and includes both static metadata and dynamic time-series measurements.&lt;/p&gt;

&lt;p&gt; Structure Overview Each JSON block includes the following components: subject_id: Unique identifier for the participant, extracted from the filename or cell A1 of the original Excel sheet.&lt;/p&gt;

&lt;p&gt; metadata: Key descriptive fields about the subject and test context. These include: age: Age of the subject at the time of testing height_cm: Height in centimeters weight_kg: Weight in kilograms sex: Biological sex test_type: Type of test performed (e.g., maximal, submaximal) test_date: Date of the test session subject_type: Classification of the subject (e.g., healthy, clinical) birth_date: Date of birth test_reason: Stated reason or objective for the test &lt;/p&gt;

&lt;p&gt; data_table: Time-series measurements recorded during the test. This includes: headers: List of variable names (e.g., VO₂, HR, GPS altitude, cadence) units: Corresponding units of measurement for each variable records: List of data rows, each representing a time point or step in the protocol. All columns are preserved to maintain raw data integrity. &lt;/p&gt;

&lt;p&gt; Each subject table includes: 5-minutes of baseline measure with the subject quietly standing; walking trial; tango trial. The beginning and endpoints of each section/trial are identified by a marker in the appropriate column. &lt;/p&gt;
description: Python, 3.11.5
DOI: 10.15468/dl.k78zk9
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Geometry POLYGON ((-120.822 36.70838, -120.8222 36.70803, -120.8223 36.70767, -120.8224 36.7073, -120.8225 36.70693, -120.8226 36.70655, -120.8227 36.70618, -120.8227 36.7058, -120.8227 36.70542, -120.8227 36.70504, -120.8226 36.70466, -120.8225 36.70429, -120.8224 36.70392, -120.8223 36.70355, -120.8221 36.70319, -120.822 36.70284, -120.8218 36.70249, -120.8216 36.70216, -120.8213 36.70183, -120.8211 36.70151, -120.8208 36.70121, -120.8205 36.70092, -120.8202 36.70064, -120.8122 36.69404, -120.8118 36.69377, -120.8115 36.69352, -120.8111 36.69329, -120.8107 36.69307, -120.8103 36.69287, -120.8099 36.69268, -120.8095 36.69251, -120.8091 36.69236, -120.8086 36.69223, -120.8082 36.69212, -120.8077 36.69203, -120.8073 36.69195, -120.8068 36.6919, -120.8063 36.69186, -120.8058 36.69185, -120.8054 36.69185, -120.8049 36.69188, -120.8044 36.69192, -120.804 36.69199, -120.8035 36.69207, -120.8031 36.69217, -120.8026 36.69229, -120.8022 36.69243, -120.8017 36.69259, -120.8013 36.69277, -120.8009 36.69296, -120.8005 36.69317, -120.8002 36.6934, -120.7998 36.69364, -120.7994 36.6939, -120.7971 36.69574, -120.7968 36.69601, -120.7965 36.69629, -120.7962 36.69658, -120.7959 36.69689, -120.7957 36.6972, -120.7954 36.69753, -120.7952 36.69786, -120.795 36.6982, -120.7949 36.69855, -120.7947 36.6989, -120.7946 36.69927, -120.7945 36.69963, -120.7945 36.7, -120.7944 36.70037, -120.7944 36.70074, -120.7944 36.70111, -120.7944 36.70149, -120.7945 36.70186, -120.7945 36.70222, -120.7946 36.70259, -120.7948 36.70295, -120.7949 36.7033, -120.7951 36.70365, -120.7953 36.70399, -120.7955 36.70432, -120.7957 36.70464, -120.7959 36.70496, -120.7962 36.70526, -120.7965 36.70555, -120.7968 36.70583, -120.7971 36.70609, -120.7975 36.70634, -120.8056 36.71197, -120.806 36.7122, -120.8063 36.71243, -120.8067 36.71263, -120.8071 36.71282, -120.8075 36.71299, -120.808 36.71315, -120.8084 36.71328, -120.8088 36.7134, -120.8093 36.7135, -120.8097 36.71359, -120.8102 36.71365, -120.8107 36.71369, -120.8111 36.71372, -120.8116 36.71372, -120.812 36.71371, -120.8138 36.71362, -120.8143 36.71358, -120.8148 36.71353, -120.8152 36.71345, -120.8157 36.71336, -120.8161 36.71325, -120.8166 36.71311, -120.817 36.71296, -120.8175 36.71279, -120.8179 36.7126, -120.8183 36.71239, -120.8187 36.71217, -120.819 36.71193, -120.8194 36.71167, -120.8197 36.7114, -120.8201 36.71112, -120.8204 36.71082, -120.8206 36.71051, -120.8209 36.71019, -120.8211 36.70985, -120.8214 36.70951, -120.8218 36.70873, -120.822 36.70838))",
 {
 "not" : {
 "TaxonKey" : [
 "is Plantae"
 ]
 }
 },
 {
 "not" : {
 "TaxonKey" : [
 "is Aves"
 ]
 }
 },
 "Year is greater than or equal to 1926",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 102 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053545-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6spu42
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Suillus placidus"
 ]
}

 The dataset includes 11 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053546-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4vfd3j
title: Occurrence Download

description: A dataset containing 16259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "GadmGid is IDN.31_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 16259 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053547-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8278z
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Geometry POLYGON ((-120.7908 36.69496, -120.7905 36.69521, -120.7901 36.69547, -120.7898 36.69575, -120.7895 36.69604, -120.7892 36.69634, -120.789 36.69665, -120.7887 36.69698, -120.7885 36.69731, -120.7883 36.69766, -120.7881 36.69801, -120.788 36.69837, -120.7878 36.69873, -120.7877 36.6991, -120.7876 36.69948, -120.7876 36.69985, -120.7876 36.70023, -120.7875 36.70061, -120.7876 36.70099, -120.7876 36.70137, -120.7877 36.70174, -120.7878 36.70212, -120.7879 36.70248, -120.788 36.70285, -120.7882 36.7032, -120.7884 36.70355, -120.7886 36.70389, -120.7888 36.70422, -120.7891 36.70454, -120.7893 36.70485, -120.7896 36.70515, -120.7899 36.70543, -120.7903 36.7057, -120.7906 36.70596, -120.791 36.7062, -120.7914 36.70643, -120.7918 36.70664, -120.7963 36.70889, -120.7967 36.70908, -120.7971 36.70926, -120.7975 36.70942, -120.798 36.70956, -120.7984 36.70968, -120.7989 36.70978, -120.7993 36.70986, -120.7998 36.70992, -120.8003 36.70996, -120.8008 36.70998, -120.8012 36.70998, -120.8017 36.70996, -120.8022 36.70993, -120.8027 36.70987, -120.8031 36.70979, -120.8036 36.70969, -120.804 36.70957, -120.8045 36.70943, -120.8049 36.70928, -120.8053 36.7091, -120.8057 36.70891, -120.8061 36.7087, -120.8065 36.70848, -120.8069 36.70824, -120.8072 36.70798, -120.8076 36.70771, -120.8092 36.70634, -120.8095 36.70606, -120.8098 36.70577, -120.8101 36.70546, -120.8103 36.70515, -120.8105 36.70483, -120.8107 36.70449, -120.8109 36.70415, -120.8111 36.7038, -120.8112 36.70345, -120.8114 36.70309, -120.8115 36.70272, -120.8115 36.70236, -120.8116 36.70198, -120.8116 36.70161, -120.8116 36.70124, -120.8116 36.70087, -120.8115 36.7005, -120.8115 36.70013, -120.8114 36.69976, -120.8113 36.6994, -120.8111 36.69905, -120.8109 36.6987, -120.8108 36.69835, -120.8106 36.69802, -120.8103 36.6977, -120.808 36.6946, -120.8077 36.69428, -120.8075 36.69398, -120.8072 36.69368, -120.8069 36.6934, -120.8065 36.69313, -120.8062 36.69288, -120.8058 36.69264, -120.8055 36.69242, -120.8051 36.69221, -120.8047 36.69202, -120.8043 36.69185, -120.8038 36.69169, -120.8034 36.69155, -120.803 36.69143, -120.8025 36.69133, -120.8021 36.69125, -120.8016 36.69118, -120.8011 36.69114, -120.8007 36.69111, -120.8002 36.69111, -120.7997 36.69112, -120.7993 36.69115, -120.7988 36.69121, -120.7984 36.69128, -120.7979 36.69137, -120.7975 36.69148, -120.797 36.6916, -120.7966 36.69175, -120.7962 36.69191, -120.7958 36.69209, -120.7954 36.69229, -120.795 36.6925, -120.7912 36.69473, -120.7908 36.69496))",
 {
 "not" : {
 "TaxonKey" : [
 "is Plantae"
 ]
 }
 },
 {
 "not" : {
 "TaxonKey" : [
 "is Aves"
 ]
 }
 },
 "Year is greater than or equal to 1926",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 99 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053552-251120083545085/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.30884618.v1
title: Corn-leaf-disease-data

description: This project shares a subset of the dataset used in the paper “IRG-ResNet: Distillation Model for Corn Disease Recognition”. The dataset subset is curated from a larger collection of corn disease images and is intended for experimental validation in corn disease recognition tasks.The dataset includes multiple representative corn disease categories. All images were collected from real field environments, covering diverse illumination conditions, viewing angles, and disease progression stages. Prior to model training and evaluation, the images were uniformly cleaned and preprocessed to ensure data quality and experimental reproducibility.This dataset subset is provided for research and educational purposes only. It is intended to support studies on lightweight deep learning models, knowledge distillation, and intelligent agricultural disease recognition, and to facilitate fair comparison and reproducibility of related methods.Usage:The dataset subset follows the experimental settings described in the original paper and can be used for image classification or object detection tasks. Details regarding data splitting, training strategies, and evaluation metrics are provided in the paper.If you use this dataset in your research, please cite:IRG-ResNet: Distillation Model for Corn Disease Recognition
DOI: 10.6084/m9.figshare.30884618
title: Corn-leaf-disease-data

description: This project shares a subset of the dataset used in the paper “IRG-ResNet: Distillation Model for Corn Disease Recognition”. The dataset subset is curated from a larger collection of corn disease images and is intended for experimental validation in corn disease recognition tasks.The dataset includes multiple representative corn disease categories. All images were collected from real field environments, covering diverse illumination conditions, viewing angles, and disease progression stages. Prior to model training and evaluation, the images were uniformly cleaned and preprocessed to ensure data quality and experimental reproducibility.This dataset subset is provided for research and educational purposes only. It is intended to support studies on lightweight deep learning models, knowledge distillation, and intelligent agricultural disease recognition, and to facilitate fair comparison and reproducibility of related methods.Usage:The dataset subset follows the experimental settings described in the original paper and can be used for image classification or object detection tasks. Details regarding data splitting, training strategies, and evaluation metrics are provided in the paper.If you use this dataset in your research, please cite:IRG-ResNet: Distillation Model for Corn Disease Recognition
DOI: 10.5281/zenodo.17853953
title: FutuRaM – Composition – Consolidated Data – Wind Turbines
DOI: 10.5281/zenodo.17853952
title: FutuRaM – Composition – Consolidated Data – Wind Turbines
DOI: 10.13130/rd_unimi/uoloji/gipdhc
title: Agosto_1936_Pagina_001.jpg

description: Bulletin of 1936/08/01
DOI: 10.13130/rd_unimi/uoloji/bl7vjq
title: Agosto_1936_Pagina_003.jpg

description: Bulletin of 1936/08/01
DOI: 10.13130/rd_unimi/uoloji/sjkzyd
title: Agosto_1936_Pagina_002.jpg

description: Bulletin of 1936/08/01
DOI: 10.13130/rd_unimi/uoloji/wxpscd
title: Agosto_1936_Pagina_004.jpg

description: Bulletin of 1936/08/01
DOI: 10.13130/rd_unimi/uoloji/1coqzv
title: Agosto_1936_Pagina_005.jpg

description: Bulletin of 1936/08/02
DOI: 10.13130/rd_unimi/uoloji/ac8xa0
title: Agosto_1936_Pagina_006.jpg

description: Bulletin of 1936/08/02
DOI: 10.48657/r7rc-tk19
title: Data from: First record of the xiphosurid Euproops cf. danae from Ireland- A newly discovered specimen from the Pennsylvanian of County Clare, Ireland
DOI: 10.13130/rd_unimi/uoloji/uy7af9
title: Agosto_1936_Pagina_007.jpg

description: Bulletin of 1936/08/02
DOI: 10.13130/rd_unimi/uoloji/2axa3h
title: Agosto_1936_Pagina_008.jpg

description: Bulletin of 1936/08/02
DOI: 10.13130/rd_unimi/uoloji/u2jhid
title: Agosto_1936_Pagina_009.jpg

description: Bulletin of 1936/08/03