Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,399
datasets available to search
ShareScore release 0.7.1
Dataset results
2,399 results for “fragmentation”
Interspecific alterations in avian physiology across a gradient of fragmentation
<b>Description: </b><p>Survey of fluctuating asymmetry and ptilochronology in understory birds across a gradient of forest degradation, with sampling transects in the interior, edge, and matrix of 10ha fragments at SAFE</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/188"><b>Alterations in avian physiology and behaviour across a gradient of fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=136">here</a></p><p><b>Files: </b>This consists of 1 file: Peel_data_2018.xlsx</p><p><b>Peel_data_2018.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Fluctuating asymmetry in bird species across a gradient of fragmentation</b> (described in worksheet fluctuating_asymmetry)</p><p>Description: This is a dataset of the lengths of tarsi of birds captured in mist nets at SAFE. Captured birds had each tarsus measured three times using digital calipers precise to .02mm. Tarsus length was measured from the notch at the back of the tibia-tarsus joint to the plane formed by bending the foot perpendicular to the tarsus. An average value was calculated for each tarsus and the absolute difference between the right and left tarsus taken as a measure of fluctuating asymmetry in an individual bird.</p><p>Number of fields: 15</p><p>Number of data rows: 711</p><p>Fields: </p><ul><li><b>band</b>: Band number of captured bird (Field type: ID)</li><li><b>species</b>: Species of captured bird (Field type: Taxa)</li><li><b>site</b>: Full name of site where bird was caught (Field type: Location)</li><li><b>location</b>: type of site at which bird was caught (Field type: Categorical)</li><li><b>fragment</b>: fragment in which bird was caught (Field type: Categorical)</li><li><b>date</b>: Date bird was captured (Field type: Datetime)</li><li><b>left1</b>: Length of left tarsus (Field type: Numeric Trait)</li><li><b>right1</b>: Length of right tarsus (Field type: Numeric Trait)</li><li><b>left2</b>: Length of left tarsus (Field type: Numeric Trait)</li><li><b>right2</b>: Length of right tarsus (Field type: Numeric Trait)</li><li><b>left3</b>: Length of left tarsus (Field type: Numeric Trait)</li><li><b>right3</b>: Length of right tarsus (Field type: Numeric Trait)</li><li><b>left.average</b>: Average of left tarsus measurements (Field type: Numeric Trait)</li><li><b>right.average</b>: Average of right tarsus measurements (Field type: Numeric Trait)</li><li><b>abs.diff</b>: Absolute difference between left and right average (Field type: Numeric Trait)</li></ul></li><li><p><b>Ptilochronological measurements of bird species across a gradient of fragmentation</b> (described in worksheet growth_bands)</p><p>Description: This is a dataset of growth bands measured on rectrice feathers of captured birds. The third rectrice feather of birds captured using mist-nets was plucked in the field and the distal end of all visible dark growth bands marked with a pin. Distance between pin marks was measured as single growth bands. This dataset contains each individual growth band, as well as averages across the whole feather, and averages for the ten growth bands proximal to the 3/4 point on the feather. </p><p>Number of fields: 40</p><p>Number of data rows: 746</p><p>Fields: </p><ul><li><b>date</b>: Date bird was captured (Field type: Date)</li><li><b>site</b>: Full name of site where bird was caught (Field type: Location)</li><li><b>location</b>: type of site at which bird was caught (Field type: Categorical)</li><li><b>fragment</b>: fragment in which bird was caught (Field type: Categorical)</li><li><b>band</b>: Band number of captured bird (Field type: ID)</li><li><b>species</b>: Species of captured bird (Field type: Taxa)</li><li><b>age</b>: Age of bird (Field type: Categorical Trait)</li><li><b>length</b>: Length of rectrice (Field type: Numeric Trait)</li><li><b>band1</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band2</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band3</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band4</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band5</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>ban6</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band7</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band8</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band9</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band10</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band11</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band12</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band13</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band14</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band15</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band16</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band17</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band18</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band19</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band20</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band21</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band22</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band23</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>band24</b>: Width of growth band (Field type: Numeric Trait)</li><li><b>total.sum</b>: sum of all growth bands measured (Field type: Numeric Trait)</li><li><b>number</b>: number of growth bands measurable in a single feather (Field type: Numeric Trait)</li><li><b>total.avg</b>: average of all measured growth bands in a feather (Field type: Numeric Trait)</li><li><b>three.quarters</b>: the three quarters point in a feather: a point three quarters along the distance from the tip of the calamus to the distal end of the feather vane (Field type: Numeric Trait)</li><li><b>ten.sum</b>: sum of the ten (or fewer) growth bands proximal to the three quarters point (Field type: Numeric Trait)</li><li><b>ten.number</b>: number of bands proximal to the three quarters point (ten maximum) (Field type: Numeric Trait)</li><li><b>ten.avg</b>: average of the ten (or fewer) bands proximal to the three quarters point (Field type: Numeric Trait)</li><li><b>sd</b>: standard deviation of all the bands present in a feather (Field type: Numeric Trait)</li></ul></li></ol><p><b>Date range: </b>1900-01-22 to 2018-05-30</p><p><b>Latitudinal extent: </b>4.6921 to 4.7293</p><p><b>Longitudinal extent: </b>117.4703 to 117.6201</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Aves<br> -  -  - Columbiformes<br> -  -  -  - Columbidae<br> -  -  -  -  - <i>Chalcophaps</i><br> -  -  -  -  -  - <i>Chalcophaps indica</i><br> -  -  - Coraciiformes<br> -  -  -  - Alcedinidae<br> -  -  -  -  - <i>Ceyx</i><br> -  -  -  -  -  - <i>Ceyx erithaca</i><br> -  -  -  -  -  -  - <i>Ceyx erithaca erithaca</i><br> -  -  - Cuculiformes<br> -  -  -  - Cuculidae<br> -  -  -  -  - <i>Cacomantis</i><br> -  -  -  -  -  - <i>Cacomantis merulinus</i><br> -  -  - Passeriformes<br> -  -  -  - Cisticolidae<br> -  -  -  -  - <i>Orthotomus</i><br> -  -  -  -  -  - <i>Orthotomus atrogularis</i><br> -  -  -  -  -  - <i>Orthotomus sericeus</i><br> -  -  -  -  - <i>Prinia</i><br> -  -  -  -  -  - <i>Prinia flaviventris</i><br> -  -  -  - Dicaeidae<br> -  -  -  -  - <i>Dicaeum</i><br> -  -  -  -  -  - <i>Dicaeum trigonostigma</i><br> -  -  -  -  - <i>Prionochilus</i><br> -  -  -  -  -  - <i>Prionochilus maculatus</i><br> -  -  -  -  -  - <i>Prionochilus xanthopygius</i><br> -  -  -  - Estrildidae<br> -  -  -  -  - <i>Lonchura</i><br> -  -  -  -  -  - <i>Lonchura atricapilla</i><br> -  -  -  -  -  - <i>Lonchura fuscans</i><br> -  -  -  - Laniidae<br> -  -  -  -  - <i>Lanius</i><br> -  -  -  -  -  - <i>Lanius tigrinus</i><br> -  -  -  - Monarchidae<br> -  -  -  -  - <i>Hypothymis</i><br> -  -  -  -  -  - <i>Hypothymis azurea</i><br> -  -  -  -  - <i>Rhipidura</i><br> -  -  -  -  -  - <i>Rhipidura javanica</i><br> -  -  -  - Muscicapidae<br> -  -  -  -  - <i>Copsychus</i><br> -  -  -  -  -  - <i>Copsychus stricklandii</i><br> -  -  -  -  - <i>Cyornis</i><br> -  -  -  -  -  - <i>Cyornis caerulatus</i><br> -  -  -  -  - <i>Enicurus</i><br> -  -  -  -  -  - <i>Enicurus leschenaulti</i><br> -  -  -  -  - <i>Luscinia</i><br> -  -  -  -  -  - <i>Luscinia cyane</i><br> -  -  -  -  - <i>Rhinomyias</i><br> -  -  -  -  -  - <i>Rhinomyias umbratilis</i><br> -  -  -  - Nectariniidae<br> -  -  -  -  - <i>Aethopyga</i><br> -  -  -  -  -  - <i>Aethopyga siparaja</i><br> -  -  -  -  - <i>Arachnothera</i><br> -  -  -  -  -  - <i>Arachnothera everetti</i><br> -  -  -  -  -  - <i>Arachnothera hypogrammicum</i><br> -  -  -  -  -  - <i>Arachnothera longirostra</i><br> -  -  -  - Pellorneidae<br> -  -  -  -  - <i>Alcippe</i><br> -  -  -  -  -  - <i>Alcippe brunneicauda</i><br> -  -  -  -  - <i>Malacocincla</i><br> -  -  -  -  -  - <i>Malacocincla malaccensis</i><br> -  -  -  -  -  - <i>Malacocincla sepiaria</i><br> -  -  -  -  - <i>Malacopteron</i><br> -  -  -  -  -  - <i>Malacopteron affine</i><br> -  -  -  -  -  - <i>Malacopteron cinereum</i><br> -  -  -  -  -  - <i>Malacopteron magnirostre</i><br> -  -  -  -  -  - <i>Malacopteron magnum</i><br> -  -  -  -  - <i>Pellorneum</i><br> -  -  -  -  -  - <i>Pellorneum bicolor</i><br> -  -  -  -  -  - <i>Pellorneum capistratum</i><br> -  -  -  - Pycnonotidae<br> -  -  -  -  - <i>Alophoixus</i><br> -  -  -  -  -  - <i>Alophoixus bres</i><br> -  -  -  -  -  - <i>Alophoixus phaeocephalus</i><br> -  -  -  -  - <i>Pycnonotus</i><br> -  -  -  -  -  - <i>Pycnonotus atriceps</i><br> -  -  -  -  -  - <i>Pycnonotus brunneus</i><br> -  -  -  -  -  - <i>Pycnonotus erythropthalmos</i><br> -  -  -  -  -  - <i>Pycnonotus goiavier</i><br> -  -  -  -  -  - <i>Pycnonotus simplex</i><br> -  -  -  -  - <i>Tricholestes</i><br> -  -  -  -  -  - <i>Tricholestes criniger</i><br> -  -  -  - Tephrodornithidae<br> -  -  -  -  - <i>Philentoma</i><br> -  -  -  -  -  - <i>Philentoma pyrhoptera</i><br> -  -  -  - Timaliidae<br> -  -  -  -  - <i>Cyanoderma</i><br> -  -  -  -  -  - <i>Cyanoderma erythropterum</i><br> -  -  -  -  - <i>Macronus</i><br> -  -  -  -  -  - <i>Macronus ptilosus</i><br> -  -  -  -  - <i>Mixornis</i><br> -  -  -  -  -  - <i>Mixornis bornensis</i><br> -  -  -  -  - <i>Pomatorhinus</i><br> -  -  -  -  -  - <i>Pomatorhinus montanus</i><br> -  -  -  -  - <i>Stachyris</i><br> -  -  -  -  -  - <i>Stachyris maculata</i><br> -  -  -  -  -  - <i>Stachyris poliocephala</i><br> -  -  - Piciformes<br> -  -  -  - Picidae<br> -  -  -  -  - <i>Sasia</i><br> -  -  -  -  -  - <i>Sasia abnormis</i><br></div><p></p>
PanDDA analysis of NUDT7 screened against DSPL and OxXChem fragment libraries
<p><strong><a href="https://www.thesgc.org/scientists/groups/oxford">SGC Oxford</a> has performed a crystallographic fragment screen on the human peroxisomal coenzyme A diphosphatase NUDT7 (<a href="http://www.uniprot.org/uniprot/P0C024">UniProtKB - P0C024</a>). All structures with clearly identifiable ligands were deposited in the <a href="http://www.wwpdb.org/">Protein Data Bank</a> under Group Deposition ID G_1002045. The final structures and the relevant PanDDA event maps can be found at the <a href="https://www.thesgc.org/fragment-screening">SGC fragment screening website</a>. This work is part of the <a href="https://www.thesgc.org/tep">Target Enabling Package (TEP)</a> program at SGC and the complete TEP for NUDT7 is will also be available on ZENODO shortly.</strong></p> <p> </p> <p><em><strong>Experiment</strong></em></p> <p>Crystals were prepared at the <a href="http://www.diamond.ac.uk/Beamlines/Mx/Fragment-Screening.html">XChem </a>facility of the <a href="http://www.diamond.ac.uk">Diamond Light Source</a> (DLS). Briefly, crystals were soaked overnight with two fragment libraries; the Diamond- SGC Poised Library set (Cox et al., 2016) and the <a href="https://xchem.github.io/oxxchem/">OxXChem</a> set with nominal fragment concentrations of 100 mM, with DMSO at 30% v/v. Additionally, a series of follow-up compounds based on an initial fragment hit was synthesized and soaked overnight with nominal compound concentrations of 30 mM, with DMSO at 30% v/v. All datasets were collected at <a href="http://www.diamond.ac.uk/Beamlines/Mx.html">MX beamlines at DLS</a>. Autoprocessed datasets were analysed by Pan-Dataset Density Analysis (PanDDA) (Pearce et al., 2017). All ligands that were clearly identifiable in PanDDA event maps were modelled, refined and deposited into the PDB.</p> <p> </p> <p><em><strong>Content</strong></em></p> <p>This repository contains:</p> <ul> <li>all results from the PanDDA analysis, including ground-state-mean maps and PanDDA event & Z-maps for all ligand bound structures</li> <li>MTZ and AIMLESS logfiles from auto-processing</li> <li>PDB, CIF & PNG files of all the soaked compounds</li> <li>final refine.pdb and refine.mtz filess of all ligand bound structures</li> <li>all data belonging to an individual crystal can be found in <em>processed_datasets/<crystal_ID></em></li> </ul> <p> </p> <p><em><strong>References</strong></em></p> <p>Cox, O. B. et al. A poised fragment library enables rapid synthetic expansion yielding the first reported inhibitors of PHIP(2), an atypical bromodomain. Chem. Sci. 7, 2322–2330 (2016).</p> <p>Pearce, N. M. et al. A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. Nat Commun 8, (2017).</p>
Artificially-generated Lecture Video Fragmentation Dataset and Ground Truth
<p>We provide a large-scale lecture video dataset consisting of artificially-generated lectures, and the corresponding ground-truth fragmentation, for the purpose of evaluating lecture video fragmentation techniques.</p> <p>For creating this dataset, 1498 speech transcript files (generated automatically by ASR software) were used from the world's biggest academic online video repository, the VideoLectures.NET. These transcripts correspond to lectures from various fields of science, such as Computer science, Mathematics, Medicine, Politics etc. In order to create the synthetic video lectures, all transcripts were randomly split in fragments, the duration of which ranges between 4 and 8 minutes. Each synthetic lecture was then assembled by combining (stitching) exactly 20 randomly selected fragments. 300 such artificially-generated lectures are included in the released dataset. Each such lecture file has a mean duration of about 120 minutes, thus the dataset contains altogether about 600 hours of artificially-generated lectures. Every pair of consecutive fragments in these lectures originally comes from different videos, consequently the point in time where such two fragments are joined is a known ground-truth fragment boundary. All these boundaries form the dataset's ground truth. We should stress that we do not generate the corresponding video files for the artificially-generated lectures (only the transcripts), and one should not try to reverse-engineer the dataset creation process so as to use in some way the visual modality for detecting the fragments in this dataset.</p> <p><strong>File format</strong></p> <p>After you download the provided .zip and unpack it, the extracted folder will contain two sub-folders:</p> <pre><code>1. ALV_srt 2. ALV_srt_GT </code></pre> <p>Each of them contains 300 files.</p> <p>The <strong>ALV_srt</strong> folder contains the transcripts of every artificially-generated lecture, in the standard SRT format:</p> <pre><code>1. A numeric counter identifying each sequential subtitle 2. The time that the subtitle should appear on the screen, followed by --> and the time it should disappear 3. Subtitle's text itself on one or more lines 4. A blank line containing no text </code></pre> <p>The <strong>ALV_srt_GT</strong> folder contains the ground truth (GT) fragments corresponding to the lectures (transcripts) of the <strong>ALV_srt</strong> folder. Each GT file consists of 3 tab-separated columns and 20 rows, in the following format:</p> <pre><code><Fragment_ID_1> <StartTime_1> <EndTime_1> <Fragment_ID_2> <StartTime_2> <EndTime_2> <Fragment_ID_3> <StartTime_3> <EndTime_3> . . . <Fragment_ID_20> <StartTime_20> <EndTime_20> </code></pre> <p>Each row indicates a fragment. The first column indicates the ID of a fragment while the second and the third column indicate the start and the end time of the fragment respectively.</p> <p><strong>License and Citation</strong></p> <p>This dataset is provided for academic, non-commercial use only. If you find this dataset useful in your work, please cite the following publication where the dataset is introduced:</p> <p><em>D. Galanopoulos, V. Mezaris, “Temporal Lecture Video Fragmentation using Word Embeddings”, Proc. 25th Int. Conf. on Multimedia Modeling (MMM2019), Thessaloniki, Greece, Jan. 2019.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreement No 693092 MOVING. We are grateful to JSI/VideoLectures.NET for providing the lectures’ transcripts.</p>
Bodhgayā, Bihar. Architectural fragment showing the death of the Buddha. Berlin, Museum für Asiatische Kunst.
<p>Bodhgayā, Bihar. Architectural fragment showing the death of the Buddha. Berlin, Museum für Asiatische Kunst.</p>
PanDDA analysis of NUDT5 screened against DSPi poised fragment library
<p><strong>A crystallographic fragment screen on the human ADP-sugar pyrophosphatase NUDT5 (UniProtKB - Q9UKK9) has been performed at the Structural Genomics Consortium (SGC). All structures with clearly identifiable ligands were deposited in the <a href="http://www.wwpdb.org/">Protein Data Bank</a> under Group Deposition ID </strong> <strong>G_1002057. The final structures and the relevant PanDDA event maps can be found at the <a href="https://www.thesgc.org/fragment-screening">SGC fragment screening website</a>. </strong></p> <p> </p> <p><em><strong>Experiment</strong></em></p> <p>The experiment has been performed at the XChem facility at the Diamond Light Source. NUDT5 crystals were soaked with concentrated solutions (500 mM) of fragments from DSPi poised fragment library at 10% v/v for 30 minutes. All datasets were collected at I04-1 at DLS. Autoprocessed datasets were analysed by Pan-Dataset Density Analysis (PanDDA) (Pearce et al., 2017). All ligands that were clearly identifiable in PanDDA event maps were modelled, refined and deposited into the PDB.</p> <p> </p> <p><em><strong>Content</strong></em></p> <p>This repository contains:</p> <ul> <li>all results from the PanDDA analysis, including ground-state-mean maps and PanDDA event & Z-maps for all ligand bound structures</li> <li>MTZ and AIMLESS logfiles from auto-processing</li> <li>PDB, CIF & PNG files of all the soaked compounds</li> <li>final refine.pdb and refine.mtz filess of all ligand bound structures</li> <li>all data belonging to an individual crystal can be found in <em>processed_datasets/<crystal_ID</em></li> </ul> <p><em><strong>References</strong></em></p> <p>Cox, O. B. et al. A poised fragment library enables rapid synthetic expansion yielding the first reported inhibitors of PHIP(2), an atypical bromodomain. Chem. Sci. 7, 2322–2330 (2016).</p> <p>Pearce, N. M. et al. A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. Nat Commun 8, (2017).</p>
Cloning and test expression of HTT fragment clones - 2019/02/28
<p><strong>Project: </strong>High resolution structural analysis of purified HTT proteins</p> <p><strong>Experiment: </strong>Cloning and test expression of HTT fragment clones</p> <p><strong>Date completed:­ </strong>2019/02/28</p> <p><strong>Rationale: </strong>I aim to complement our previous work on purifying stable regions of huntingtin by cloning further regions of huntingtin corresponding to discrete HEAT repeats for eukaryotic expression. Constructs will initially be screened by BVES in insect sf9 cells. High quality constructs will undergo extensive crystallization experiments and structure determination by X-ray crystallography using well established high-throughput and systematic protocols in place at the SGC. Any solved structures may allow generation of a pseudoatomic resolution (<4 Å) model of the HTT protein using the cryoEM model as a guide. </p>
PanDDA analysis of DCP2B screened against DSPL/DSi Poised, OxXChem fragment libraries and initial follow up chemistry
<p><strong><a href="https://www.thesgc.org/scientists/groups/oxford">SGC Oxford</a> has performed a crystallographic fragment screen, and initial follow up chemistry on the </strong><strong>Human m7GpppN-mRNA Hydrolase (DCP2/NUDT20, <a href="https://www.uniprot.org/uniprot/Q8IU60">UniProtKB - QIU60</a>). All structures with clearly identifiable ligands were deposited in the <a href="http://www.wwpdb.org/">Protein Data Bank</a> under Group Deposition ID G_1002061, the corresponding apo structures are deposited under Group Deposition ID G_1002062. </strong></p> <p><em><strong>Experiment</strong></em></p> <p>Crystals were prepared at the <a href="http://www.diamond.ac.uk/Beamlines/Mx/Fragment-Screening.html">XChem </a>facility of the <a href="http://www.diamond.ac.uk">Diamond Light Source</a> (DLS). Briefly, crystals were soaked overnight with two fragment libraries; the Diamond- SGC Poised Library set (Cox et al., 2016) and the <a href="https://xchem.github.io/oxxchem/">OxXChem</a> set with nominal fragment concentrations of 100 mM, with DMSO at 20% v/v. Additionally, a series of follow-up compounds based on an initial fragment hit was synthesized and soaked overnight with nominal compound concentrations of 10-200 mM, with DMSO at 20% v/v. All datasets were collected at <a href="http://www.diamond.ac.uk/Beamlines/Mx.html">MX beamlines at DLS</a>. Autoprocessed datasets were analysed by Pan-Dataset Density Analysis (PanDDA) (Pearce et al., 2017). All ligands that were clearly identifiable in PanDDA event maps were modelled, refined and deposited into the PDB.</p> <p><em><strong>Content</strong></em></p> <p>This repository contains:</p> <p><em><strong>Modelled Data</strong></em></p> <ul> <li>Organised by crystal identifier, each folder contains: <ul> <li>Autoprocessing data from Diamond Light Source automated pipelines (including MTZ)</li> <li>PDB, CIF & PNG files of all the soaked compounds</li> <li>PanDDA event maps</li> <li>Final refine.pdb and refine.mtz files of all ligand bound structure <ul> <li>Superposed structures (refine.pdb)</li> <li>Separated bound & ground states (refine.split.bound.pdb & refine.split.ground.pdb)</li> </ul> </li> </ul> </li> </ul> <p><em><strong>PanDDA Analysis Data</strong></em></p> <ul> <li>This is split into two directories. This split is only due to technical limitations at the time of preparation of the data, and the timeliness of the data. <ul> <li>All results from the PanDDA analysis, including ground-state-mean maps and PanDDA event & Z-maps for all ligand bound structures. </li> </ul> </li> </ul>
Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation
<p>Data of Ecological Application paper "Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation"</p>
ROS-Specific Huntingtin Interactions: Chromatin retention assay with huntingtin fragments containing PBM3
<p>Huntingtin amino acids 1790-1798 make up a potential PAR binding motif (PBM3). Two huntingtin fragments (1208-1810 and 1775-2413) were tested for chromatin retention upon oxidative stress.</p>
Fig. 1 in Chemical control of leaf-cutting ants: how do workers disperse toxic bait fragments onto fungus garden?
Fig. 1. Variograms columns: control, sulfluramid and indoxacarb.
Figure 1 in Diurnal activity pattern of age-sex groups of a small and fragmented population of Blackbuck (Antilope cervicapra L.) in Western Haryana, India
Figure 1. Map of the study site.
Figure 2 in Diurnal activity pattern of age-sex groups of a small and fragmented population of Blackbuck (Antilope cervicapra L.) in Western Haryana, India
Figure 2. (a) Adult male, (b) Sub adult female, (c) Adult females and sub adult
Fig. 4 in Diversity of anurans in forest fragments of southwestern Ethiopia: The case of the Yayu Coffee Forest Biosphere Reserve (YCFBR)
Fig. 4. Species cumulative curve.
Fragment coordinates from shallow WGS of colorectal cancer from patients in Pakistan
<p>BED files indicating fragment coordinates from whole genome sequencing of FFPE tumors and adjacent normal tissue samples. Sequencing data was aligned to hg19 using bwa-mem, prior to conversion to BED format. Tumor samples are indicated with suffix "_T" and normal samples are indicated with suffix "_N"</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Population Dynamics of a Subpopulation of Lion-tailed Macaques in a Rainforest Fragment, in the southern Western Ghats of India
<p>The dataset includes absolute group counts of five groups of lion-tailed macaques inhabiting the Puthuthottam forest fragment of Valparai, Tamil Nadu, India. The counts were conducted over a period of three years between 2018-2020 at six-monthly intervals. </p>
Fig. 4 in A spinose appendage fragment of a problematic arthropod from the Early Ordovician of Morocco
Fig. 4. Reconstruction of Pseudoangustidontus duplospineus gen. et sp. nov.
Figure 33. Indeterminate Actinopterygii fragments. Hypotype, UCMP 218647 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 33. Indeterminate Actinopterygii fragments. Hypotype, UCMP 218647.
Data from "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"
<p>Data from PRC article "Examination of how properties of a fissioning system impact isomeric yield ratios of the fragments"<br>https://doi.org/10.1103/PhysRevC.108.064602</p>
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
<p>This Zenodo record contains the dataset and model weights for "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing".</p> <p> </p> <p>This repository contains the following files:</p> <ul> <li> <p>For the human dataset by Wang et al.:</p> <ul> <li> <p>train_val_test_split.csv containing the mapping of the correct peptide by MaxQuant to either train, validation or test set</p> </li> <li> <p>psms_train_val_test.csv containing the mapping of correct PSMs (scan number, raw file and correct peptide by MaxQuant) to either train, validation or test set</p> </li> <li> <p>updated_spectralis_test_out.csv as before containing Spectralis-EA predictions and scores on test set, as well as initial peptides and scores by Casanovo and Novor and now containing also correct peptides by MaxQuant and Spectralis-scores on the combination of Casanovo and Novor sequences (column named spectralis_score_onlyRescoring)</p> </li> <li> <p>spectralis_test_out_heart_analysis.csv subset of 20220822_spectralis_test_out.csv containing only PSMs for the tissue heart with the computation of precision and recall values</p> </li> <li> <p>spectralis_test_out_pointnovo_deepnovo.csv containing predictions by DeepNovo and PointNovo with original scores and Spectralis-score, as well as correct peptides by MaxQuant</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>For the nine-species dataset by Tran et al.:</p> <ul> <li> <p>spectralis_ninespecies_out.csv containing spectrum identifiers, correct peptides by PEAKSDB, predicted peptides by the different de novo sequencing tools as well as original scores and Spectralis-scores for the different PSMs.</p> </li> </ul> </li> </ul>
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.