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Metabolomics Peak Tables of push-pull and control field maize tissue extracts
<p>Data tables of the WP3 maize extracts of the UPSCALE project. Fragment spectra are provided as mgf files.</p>
Table 1 in Momordica charantia L. extracts against Aedes aegypti larvae
<p><b>Table 1.</b> Duration of development (A) and mortality (B) of <i>Aedes aegypti</i> in L3 larvae treated in a rearing environment with crude ethyl acetate extract (AcOEt) from the flowers and fruits of Momordica charantia.</p><table><thead><tr><th><b>Application</b></th><th colspan="2"><b>Larval (days)</b></th><th></th><th></th><th><b>Pupal (days)</b></th><th></th><th colspan="2"><b>L3-Adult (days)</b></th><th></th></tr></thead><tbody><tr><th><b>1A</b></th><td><b>X ± SD</b></td><td><b>R</b></td><td></td><td><b>X ± SD</b></td><td><b>R</b></td><td></td><td><b>X ± SD</b></td><td><b>R</b></td><td></td></tr><tr><th>Control</th><td>12.2±2a</td><td>4-15</td><td></td><td>1.7±0.5a</td><td>1-3</td><td></td><td>13.5±1.8a</td><td>5-16</td><td></td></tr><tr><th>Testimony</th><td>11±2b</td><td>6-14</td><td></td><td>2.3±0.7b</td><td>1-4</td><td></td><td>13±1.9ab</td><td>9-15</td><td></td></tr><tr><th>100 µg/mL</th><td>7±1.2c****</td><td>5-8</td><td></td><td>2.8±0.9b</td><td>2-5</td><td></td><td>11±1.8c***</td><td>8-13</td><td></td></tr><tr><th>200 µg/mL</th><td>10±1.4bc</td><td>9-11</td><td></td><td>3±1.4b</td><td>2-4</td><td></td><td>13±2.8ab</td><td>11-15</td><td></td></tr><tr><th></th><td></td><td><b>L3</b></td><td></td><td></td><td><b>L4</b></td><td></td><td></td><td><b>Pupa</b></td><td></td></tr><tr><th><b>1B</b></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td></tr><tr><th>Control</th><td>0 ± 0a</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3 ± 0.5</td><td>13-13</td><td>1.7</td></tr><tr><th>Testimony</th><td>0 ± 0ab</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>100 µg/mL</th><td>17 ± 1.0c***</td><td>2-7</td><td>86.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>200 µg/mL</th><td>19 ± 1.0d***</td><td>2-5</td><td>96.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table><p>Experiments with 20 <i>A. aegypti</i> larvae (L3) for each test and control group were performed in triplicate and with three repetitions. Mean and standard deviation (X ±SD).Range (R). Values followed by the same letter do not present significant differences. Significance levels through the Tukey test, represented as ****P<0.0001; ***P<0.0001.</p>
Table 3 in Momordica charantia L. extracts against Aedes aegypti larvae
<p><b>Table 3.</b> Duration of the development (A) and mortality (B) of <i>Aedes aegypti</i> in L3 larvae treated in a rearing environment with crude methanol (MeOH) extract from the flowers and fruits of <i>Momordica charantia</i>.</p><table><thead><tr><th><b>Application</b></th><th colspan="2">Larval (days)</th><th></th><th colspan="2">Pupal (days)</th><th></th><th colspan="2"><b>L3-Adult (days)</b></th><th></th></tr></thead><tbody><tr><th><b>3A</b></th><td>X ± SD</td><td>R</td><td></td><td>X ± SD</td><td>R</td><td></td><td>X ± SD</td><td><b>R</b></td><td></td></tr><tr><th>Control</th><td>13.2±1.9a</td><td>5-16</td><td></td><td>1.7±0.6a</td><td>1-3</td><td></td><td>14.5±1.8a</td><td>6-17</td><td></td></tr><tr><th>Testimony</th><td>11.8±1.9b</td><td>6-14</td><td></td><td>1.6±0.6b</td><td>1-4</td><td></td><td>13.5±1.8b</td><td>7-16</td><td></td></tr><tr><th>100 µg/mL</th><td>11.2±1.9b</td><td>7-13</td><td></td><td>2.1±0.4c**</td><td>2-4</td><td></td><td>13.4±1.7b</td><td>9-15</td><td></td></tr><tr><th>200 µg/mL</th><td>8.5±0.9c****</td><td>7-11</td><td></td><td>4.5±2.2d****</td><td>1-7</td><td></td><td>13±2.5b</td><td>9-15</td><td></td></tr><tr><th></th><td></td><td>L3</td><td></td><td></td><td>L4</td><td></td><td></td><td><b>Pupa</b></td><td></td></tr><tr><th><b>3B</b></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td><td><b>X ± SD</b></td><td><b>R</b></td><td><b>%</b></td></tr><tr><th>Control</th><td>0a</td><td>0</td><td>0</td><td>0a</td><td>0</td><td>0</td><td>0.3 ± 0.5a</td><td>13-13</td><td>1.7</td></tr><tr><th>Testimony</th><td>0ab</td><td>0</td><td>0</td><td>0ab</td><td>0</td><td>0</td><td>0ab</td><td>0</td><td>0</td></tr><tr><th>100µg/mL</th><td>0.3 ± 0.5ab</td><td>1-1</td><td>1.7</td><td>2 ± 3ab</td><td>6-6</td><td>10</td><td>0.3 ± 0.5ab</td><td>1-1</td><td>3.3</td></tr><tr><th>200µg/mL</th><td>13 ± 4c***</td><td>1-6</td><td>70</td><td>1 ± 0ab</td><td>1-2</td><td>8.3</td><td>1 ± 1ab</td><td>2-2</td><td>1.7</td></tr></tbody></table><p>Experiments with 20 <i>Ae. aegypti</i> larvae (L3) for each test and control group were performed in triplicate and with three repetitions. Mean and standard deviation (X ± SD).Range (R). Values followed by the same letter do not present significant differences.Significance levels through the Tukey test, represented as **** P<0.0001; ***P<0.001; **P <0.01 vs testimony control MeOH:DMSO (1:3).</p>
Table 2 in Momordica charantia L. extracts against Aedes aegypti larvae
<p><b>Table 2.</b> Duration of development (A) and mortality (B) of <i>Aedes aegypti</i> in L3 larvae treated in a rearing environment with crude ethyl acetate extract (AcOEt) from the flowers and fruits of Momordica charantia.</p><table><thead><tr><th><b>Application</b></th><th colspan="2"><b>Larval (days)</b></th><th></th><th colspan="2"><b>Pupal (days)</b></th><th></th><th colspan="2"><b>L3-Adult (days)</b></th><th></th></tr></thead><tbody><tr><th><b>2A</b></th><td><b>X ± SD</b></td><td><b>R</b></td><td></td><td><b>X ± SD</b></td><td><b>R</b></td><td></td><td><b>X ± SD</b></td><td><b>R</b></td><td></td></tr><tr><th>Control</th><td>11.7±4.8ª</td><td>2-23</td><td></td><td>2.8±0.7a</td><td>1-5</td><td></td><td>14.4±4.9a</td><td>5-25</td><td></td></tr><tr><th>Testimony</th><td>12.5±3.1ab</td><td>7-19</td><td></td><td>2.9±0.6ab</td><td>2-4</td><td></td><td>15.3±3.3ab</td><td>9-22</td><td></td></tr><tr><th>1 µg/mL</th><td>13.1±4.4ab</td><td>5-22</td><td></td><td>2.4±0.8ac*</td><td>1-4</td><td></td><td>15.7±4.5ab</td><td>8-24</td><td></td></tr><tr><th>10 µg/mL</th><td>10±1.9ac**</td><td>7-15</td><td></td><td>3±1.5ab</td><td>1-9</td><td></td><td>12.9±2.8ac*</td><td>8-21</td><td></td></tr><tr><th>50 µg/mL</th><td>8.1±1.7c****</td><td>5-11</td><td></td><td>2.6±0.8ab</td><td>1-4</td><td></td><td>10.8±1.9d****</td><td>8-14</td><td></td></tr><tr><th></th><td></td><td><b>L3</b></td><td></td><td></td><td><b>L4</b></td><td></td><td></td><td><b>Pupa</b></td><td></td></tr><tr><th><b>2B</b></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td>X ± SD</td><td>R</td><td>%</td><td>X ± SD</td><td>R</td><td>%</td><td>X ± SD</td><td>R</td><td><b>%</b></td></tr><tr><th>Control</th><td>0.3 ± 0.5ª</td><td>1-1</td><td>3.3</td><td>0.3 ± 0.5a</td><td>1-1</td><td>1.7</td><td>0.3 ± 0.5a</td><td>1-1</td><td>1.7</td></tr><tr><th>Testimony</th><td>0.3 ± 0.5ab</td><td>1-1</td><td>1.7</td><td>0a</td><td>0-0</td><td>0</td><td>1 ± 0a</td><td>1-1</td><td>5</td></tr><tr><th>1 µg/mL</th><td>1 ± 1ab</td><td>1-3</td><td>6.7</td><td>2 ± 2a</td><td>1-4</td><td>10</td><td>0a</td><td>0</td><td>0</td></tr><tr><th>10 µg/mL</th><td>2 ± 3ab</td><td>1-5</td><td>10</td><td>1.2 ± 1a</td><td>1-2</td><td>5</td><td>0.3 ± 0.5a</td><td>1-1</td><td>2</td></tr><tr><th>50 µg/mL</th><td>8 ± 4c**</td><td>3-12</td><td>40</td><td>3 ± 0.5a</td><td>2-3</td><td>13.3</td><td>0.3 ± 0.5a</td><td>1-1</td><td>3.5</td></tr></tbody></table><p>Experiments with 20 <i>A. aegypti</i> larvae (L3) for each test and control group were performed in triplicate and with three repetitions. Mean and standard deviation (X ±SD).Range (R). Values followed by the same letter do not present significant differences. Significance levels through the Tukey test, represented as ****P<0.0001; **P<0.01; *P<0.1 vs testimony control AcOEt:DMSO (1:3).</p>
Sorption strategies for recovering critical raw materials: Extracting trace elements from saltworks brines
<p><span>Rising population, industrialisation, and resource demand intensify resource limitations, particularly critical for European Union (EU) industries dependent on raw materials. Based on economic importance and supply risk, 32 Critical Raw Materials (CRMs) and two strategic raw materials were identified in 2023. With most CRMs sourced outside the EU, sea mining emerges as a promising secondary resource for CRMs extraction from seawater, although the harvest of Trace Elements (TEs) will require energy-intensive processes. Several EU-funded projects explore circular economy and resource recovery, considering sorption methods’ potential to extract TEs from brines. Commercial polymeric (IRC747, S940, MTX8010) and inorganic (SbTreat, SrTreat) sorbents, plus another synthesised inorganic one (CuHCF), were assessed for recovering TEs (cobalt, gallium, germanium, rubidium, strontium, caesium) from saltworks brines (bitterns). Polymeric sorbents, containing chelating functional groups, effectively targeted cobalt, gallium, and strontium, commercial inorganic sorbents favoured cobalt, gallium, germanium and strontium, and CuHCF targeted rubidium and caesium. Kinetic batch experiments demonstrated rapid element retention (≤30 min) by most sorbents. In dynamic column experiments, high sorption capacities were observed for cobalt and gallium with the polymeric sorbents, particularly for aminophosphonic sorbents (IRC747 and S940) (≥2.1 mg/g). SrTreat exhibited also a high sorption capacity of 7 mg/g for strontium, SbTreat achieved 20 mg/g for gallium and germanium and CuHCF retained rubidium (10 mg/g) and caesium (70 mg/g). Acidic desorption effectively recovered (>70 %) most of the elements from the sorbents, achieving concentration factors up to 708 for cobalt with IRC747 and S940, highlighting the potential valorisation of saltworks bitterns.</span></p>
Table 2 in Phytotoxic activity of extracts obtained from cagaita (Eugenia dysenterica DC. - Myrtaceae) on the growth of black-jack (Bidens pilosa L.)
<p>Table 2. Effect of different leaf and stem bark extracts of <i>Eugenia dysenterica</i> DC. on the germination speed index (GSI) of <i>Bidens pilosa</i> L. seeds.</p><table><tbody><tr><th><b>Concentration (<b>mgL-</b> 1<b>)</b></b></th><th><b>LAE</b></th><th><b>LEE</b></th><th><b>LHE 70:30</b></th><th><b>LHE 50:50</b></th><th><b>SAE</b></th><th><b>SEE</b></th><th><b>SHE 70:30</b></th><th><b>SHE 50:50</b></th></tr></tbody><tbody><tr><th>0</th><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td><td>18.20 A</td></tr><tr><th>250</th><td>15.10 B</td><td>13.60 B</td><td>9.95 B</td><td>7.30 B</td><td>10.60 B</td><td>13.30 B</td><td>12.90 B</td><td>14.20 B</td></tr><tr><th>500</th><td>19.61 A</td><td>12.20 B</td><td>8.18 B</td><td>9.60 B</td><td>9.65 B</td><td>12.20 B</td><td>8.41 C</td><td>9.79 C</td></tr><tr><th>1000</th><td>11.75 C</td><td>7.70 C</td><td>7.61 B</td><td>9.94 B</td><td>9.40 B</td><td>8.04 C</td><td>8.10 C</td><td>9.68 C</td></tr></tbody></table><p>Means followed by the same letter in the column do not differ from each other by the Tukey test at 5% probability. Leaf Aqueous Extract (LAE), Leaf Ethanolic Extract (LEE), Leaf Hydroalcoholic Extract 70:30 (LHE 70:30), Leaf Hydroalcoholic Extract 50:50 (LHE 50:50), Stem aqueous extract (SAE), Stem ethanolic extract (SEE), Stem hydroalcoholic extract 70:30 (SHE 70:30), and Stem hydroalcoholic extract 50:50 (SHE 50:50).</p>
Table 1 in Phytotoxic activity of extracts obtained from cagaita (Eugenia dysenterica DC. - Myrtaceae) on the growth of black-jack (Bidens pilosa L.)
<p><b>Table 1</b>. Effect of different leaf and stem bark extracts of <i>Eugenia dysenterica</i> DC. on the germination percentage (G%) of <i>Bidens pilosa</i> L. seeds.</p><table><tbody><tr><th><b>Concentration (<b>mgL-</b> 1<b>)</b></b></th><th><b>LAE</b></th><th><b>LEE</b></th><th><b>LHE 70:30</b></th><th><b>LHE 50:50</b></th><th><b>SAE</b></th><th><b>SEE</b></th><th><b>SHE 70:30</b></th><th><b>SHE 50:50</b></th></tr></tbody><tbody><tr><th>0</th><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td><td>64.00 A</td></tr><tr><th>250</th><td>59.84 A</td><td>59.14 A</td><td>60.33 A</td><td>59.35 A</td><td>59.18 A</td><td>58.81 A</td><td>58.16 A</td><td>59.11 A</td></tr><tr><th>500</th><td>57.48 A</td><td>59.76 A</td><td>41.56 B</td><td>42.00 B</td><td>57.00 A</td><td>57.25 A</td><td>48.16 B</td><td>42.48 B</td></tr><tr><th>1000</th><td>58.75 A</td><td>57.70 A</td><td>40.18 B</td><td>41.14 B</td><td>59.40 A</td><td>58.14 A</td><td>42.12 B</td><td>41.18 B</td></tr></tbody></table><p>Means followed by the same letter in the column do not differ from each other by the Tukey test at 5% probability. Leaf Aqueous Extract (LAE), Leaf Ethanolic Extract (LEE), Leaf Hydroalcoholic Extract 70:30 (LHE 70:30), Leaf Hydroalcoholic Extract 50:50 (LHE 50:50), Stem aqueous extract (SAE), Stem ethanolic extract (SEE), Stem hydroalcoholic extract 70:30 (SHE 70:30), and Stem hydroalcoholic extract 50:50 (SHE 50:50).</p>
Geoparsing with Large Language Models: Leveraging the linguistic capabilities of generative AI to improve geographic information extraction
<h2>Geoparsing with Large Language Models</h2> <p>The .zip file included in this repository contains all the code and data required to reproduce the results from our paper. Note, however, that in order to run the OpenAI models, users will required an OpenAI API key and sufficient API credits.</p> <div> <h3>Data</h3> <p>The data used for the paper are in the <code>datasetst</code> and <code>results</code> folders.</p> <ul> <li> <p>**Datasets: **This contains the XML files (LGL and Geovirus) and Json files (News2024) used to benchmark the models. It also contains all the data used to fine-tune the gpt-3.5 model, the prompt templates sent to the LLMs, and other data used for mapping and data creation.</p> </li> <li> <p>**Results: **This contains the results for the models on the three datastes. The folder is separated by dataset, with a single <code>.csv</code> file giving the results for each model on each dataset separately. The <code>.csv</code> file is structured so that each row contains either a predicted toponym and an associated true toponym (along with assigned spatial coordinates), if the model correctly identified a toponym; otherwise the true toponym columns are empty for false positives and the predicted columns are empty for false negatives.</p> </li> </ul> <h3>Code</h3> <p>The code is split into two seperate folders <code>gpt_geoparser</code> and <code>notebooks</code>.</p> <ul> <li>**GPT_Geoparser: **this contains the classes and methods used process the XML and JSON articles (<code>data.py</code>), interact with the Nominatim API for geocoding (<code>gazetteer.py</code>), interact with the OpenAI API (<code>gpt_handler.py</code>), process the outputs from the GPT models (<code>geoparser.py</code>) and analyse the results (<code>analysis.py</code>).</li> <li><strong>Notebooks</strong>: This series of notebooks can be used to reproduce the results given in the paper. The file names a reasonably descriptive of what they do within the context of the paper.</li> </ul> <h3>Code/software</h3> <h3>Requirements</h3> <ul> <li>Numpy</li> <li>Pandas</li> <li>Geopy</li> <li>Scitkit-learn</li> <li>lxml</li> <li>openai</li> <li>matplotlib</li> <li>Contextily</li> <li>Shapely</li> <li>Geopandas</li> <li>tqdm</li> <li>huggingface_hub</li> <li>Gnews</li> </ul> <h3>Access information</h3> <p>Other publicly accessible locations of the data:</p> <ul> <li>The LGL and GeoVirus datasets can also be obtained <a href="https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation" target="_blank" rel="noopener">here<span> (opens in new window)</span></a>.</li> </ul> <h3>Abstract</h3> <div> <p>Geoparsing- the process of associating textual data with geographic locations - is a key challenge in natural language processing. The often ambiguous and complex nature of geospatial language make geoparsing a difficult task, requiring sophisticated language modelling techniques. Recent developments in Large Language Models (LLMs) have demonstrated their impressive capability in natural language modelling, suggesting suitability to a wide range of complex linguistic tasks. In this paper, we evaluate the performance of four LLMs - GPT-3.5, GPT-4o, Llama-3.1-8b and Gemma-2-9b - in geographic information extraction by testing them on three geoparsing benchmark datasets: GeoVirus, LGL, and a novel dataset, News2024, composed of geotagged news articles published outside the models' training window. We demonstrate that, through techniques such as fine-tuning and retrieval-augmented generation, LLMs significantly outperform existing geoparsing models. The best performing models achieve a toponym extraction F1 score of 0.985 and toponym resolution accuracy within 161 km of 0.921. Additionally, we show that the spatial information encoded within the embedding space of these models may explain their strong performance in geographic information extraction. Finally, we discuss the spatial biases inherent in the models' predictions and emphasize the need for caution when applying these techniques in certain contexts.</p> </div> <h3>Methods</h3> <div> <p>This contains the data and codes required to reproduce the results from our paper. The LGL and GeoVirus datasets are pre-existing datasets, with references given in the manuscript. The News2024 dataset was constructed specifically for the paper. </p> <p>To construct the News2024 dataset, we first created a list of 50 cities from around the world which have population greater than 1000000. We then used the GNews python package <a href="https://pypi.org/project/gnews/" target="_blank" rel="noopener">https://pypi.org/project/gnews/<span> (opens in new window)</span></a> to find a news article for each location, published between 2024-05-01 and 2024-06-30 (inclusive). Of these articles, 47 were found to contain toponyms, with the three rejected articles referring to businesses which share a name with a city, and which did not otherwise mention any place names.</p> <p>We used a semi autonmous approach to geotagging the articles. The articles were first processed using a Distil-BERT model, fine tuned for named entity recognicion. This provided a first estimate of the toponyms within the text. A human reviewer then read the articles, and accepted or rejected the machine tags, and added any tags missing from the machine tagging process. We then used OpenStreetMap to obtain geographic coordinates for the location, and to identify the toponym type (e.g. city, town, village, river etc). We also flagged if the toponym was acting as a geo-political entity, as these were reomved from the analysis process. In total, 534 toponyms were identified in the 47 news articles. </p> </div> </div>
Self-assembled molecules for hole extraction in efficient inverted PbS quantum dot solar cells
Open the record for dataset details and reuse information.
Example raw quantification outputs used for extracting view data
This data contains the raw quantification outputs from platforms FragPipe, Maxquant, DIA-NN and Spectronaut. We use them as inputs for extracting view data serving as inputs to our newly designed multi-view proteomics framework.
Extract of the project data from the LIFE KPI webtool. Deliverable 2.5 of the LIFE NatuReef project: Nature-based reef solution for coastal protection and marine biodiversity enhancement. LIFE22-NAT-IT-LIFE-NatuReef/101113742
<p>Key performance indicator, a quantifiable measure of performance over time for a specific objective.</p>
Weekly supervised Multilingual Data Set to train Named Entity Recognition for Symptom Extraction
<p>Data Sets were generated using the Weakly Supervised NER pipeline (https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) to train the symptom extraction NER models. </p> <p><strong>Supported Languages and dataset locations for the specific language:</strong></p> <p> English (base language): https://huggingface.co/HUMADEX/english_medical_ner<br> German: https://huggingface.co/HUMADEX/german_medical_ner<br> Italian: https://huggingface.co/HUMADEX/italian_medical_ner<br> Spanish: https://huggingface.co/HUMADEX/spanish_medical_ner<br> Greek: https://huggingface.co/HUMADEX/german_medical_ner<br> Slovenian: https://huggingface.co/HUMADEX/slovenian_medical_ner<br> Polish: https://huggingface.co/HUMADEX/polish_medical_ner<br> Portuguese: https://huggingface.co/HUMADEX/portugese_medical_ner</p> <p> </p> <p><strong>Dataset Building </strong></p> <ul> <li>Data Integration and Preprocessing</li> <li>Data Cleaning</li> <li>Annotation with Stanza's i2b2 Clinical Model </li> <li>Translation into the targeted language</li> <li>Word Alignment </li> <li>Data Augmentation </li> </ul> <p><strong>Acknowledgement</strong><br>This dataset had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.</p> <p><strong>Authors:</strong><br>dr. Izidor Mlakar, Rigona Sallauka, dr. Umut Arioz, dr. Matej Rojc</p> <p><strong>Please cite as:</strong></p> <p><span>Article title: Weakly-Supervised Multilingual Medical NER For Symptom Extraction For Low-Resource Languages</span><br><span>Doi: 10.20944/preprints202504.1356.v1</span><br><span>Website: </span><a title="https://www.preprints.org/manuscript/202504.1356/v1" href="https://www.preprints.org/manuscript/202504.1356/v1">https://www.preprints.org/manuscript/202504.1356/v1</a></p>
All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils
<p>The present file contains the information collected on particles analyzed in the study:</p> <p><em><strong><span><span><span><span><span><span>All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soil</span></span></span></span></span></span><span>s</span></strong></em></p> <p><span>published in the Microplastics and Nanoplastics journal in 2024. </span></p> <p><span>The first sheet of the excel file contains the data, the second sheet contains the description of the data and the parameters recorded. </span></p>
Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution) Authors: Francisco J. Pérez-Invernon, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, Patrick Jöckel and Eric J. Bucsela Description of the data CTR simulations: CTR_*.nc files LNOfs simulation: LNOfs_*.nc files *tr_*.nc: Monthly averaged trace gases *lnox*.nc: Monthly averaged lightning data<br>*ECHAM5*.nc: Monthly averaged dynamical variables<br>*grid_def*.nc: Monthly averaged grid variables<br>*tropop*.nc: Monthly averaged tropospheric variables </pre> <pre>File format: netcdf</pre> <p> </p>
Annotated Dataset for Named Entity Recognition and Relation Extraction in French Building Technical Specifications (BTS)
<p>This dataset contains 233 raw requirements extracted from French Building Technical Specifications (BTS), referred to as "<a href="https://www.aglo.ai/cctp/#:~:text=Le%20CCTP%20(Cahier%20des%20Clauses%20Techniques%20Particuli%C3%A8res)%20est%20un%20document,code%20du%20march%C3%A9%20public%20donc."><em>Cahier des Clauses Techniques Particulières (CCTP)</em></a>", specifically focused on carpentry ("<em>lot menuiserie</em>") in public French construction projects. The requirements have been collected from 72 CCTP documents, resulting in a total of 19,725 sentences and 651,948 words.</p> <p>The dataset has been annotated using <a title="Open-source text annotation tool" href="https://github.com/doccano/doccano">Doccano </a>for Named Entity Recognition (NER) and Relation Extraction (RE). The annotations involve identifying entities and the relationships between them within the domain of building requirements. This dataset is intended for research on Natural Language Processing (NLP) models for Requirements Engineering (RE) in the Architecture, Engineering, and Construction (AEC) sector. Potential applications include requirements extraction, compliance analysis, and knowledge management in construction.</p> <p>The dataset includes the following components:</p> <ol> <li><strong>CCTP Documents</strong>: The original CCTP files from which the raw requirements were extracted.</li> <li><strong>Annotated Dataset</strong>: A JSONLines file containing the annotated dataset, including labels for Named Entity Recognition (NER) and Relation Extraction (RE).</li> </ol> <p>Key features of the dataset:</p> <ul> <li>Language: French</li> <li>Number of requirements: 233</li> <li>Number of sentences: 19,725</li> <li>Number of words: 651,948</li> <li>Annotation tasks: Named Entity Recognition (NER) and Relation Extraction (RE)</li> </ul> <p>This dataset is relevant for NLP research focused on structured information extraction from domain-specific texts in the construction industry.</p>
Laptop Ratings and Specifications Extracted from PCBox
<p>This dataset contains detailed information on 100 laptops extracted from the online store PCBox using web scraping techniques. The data includes the product name, price, category, brand, average rating, number of reviews, as well as the highest and lowest ratings with associated review comments. This information enables analysis and comparison of different laptop models based on price, popularity, and user ratings.</p>
Effects of Pleurotus ferulae water extract (PWE) or Pleurotus ferulae ethanol extract (PEE) on lipid accumulation in 3T3-L1 cells.
<p><span>Lipid accumulation was measured in 3T3-L1 adipocytes following Oil Red O staining, both with and without PWE or PEE treatment. Results are expressed as the mean ± standard deviation (SD) (n = 3). Different letters indicate significant differences (p < 0.05), as determined by one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test.</span></p>
Assessment of animal diseases caused by bacteria resistant to antimicrobials: Dogs and cats - Appendix B: Excel file with all data extracted
<p>Information on all the full-text studies that were assessed, including the reason for exclusion for those that were excluded at the full-text screening and the data extracted from the included studies, can be consulted here. </p> <p>The extensive literature review was carried out by the University of Copenhagen under the contract OC/EFSA/ALPHA/2020/02 – LOT 1 (https://ted.europa.eu/udl?uri=TED:NOTICE:457654-2020:TEXT:EN:HTML)</p>
ReqExp: BERT-based ML Model for Extracting Software Requirements
<p>Datasets that were used during experiments in ReqExp project.</p>
Raw data extracted from ChEMBL
<p>Raw data files extracted from ChEMBL for the MELLODDY project.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.